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Working paper

Education and Māori Relative Income Levels Over Time: The Mediating Effect of Occupation, Industry, Hours of Work and Locality (WP 02/17)

New Zealand Treasury Working Paper 02/17

Author: Sholeh A. Maani

Abstract

This paper examines ethnic differences in the relationship between educational attainment and income in New Zealand over the period 1986 to 1996. In particular, it uses a 50% sample from the Census in each of those years to determine how far ethnic differences in income are explained by educational qualifications, access to higher paying occupations and industries, hours of work, locality of residence and marital status. The study is restricted to all those employed.

Over the period under study, the gap between Māori and European incomes increased. This reflects Māori lower educational qualifications and concentration in occupations and industries that experienced low employment growth at a time when income returns to educational qualifications increased. Those with higher educational qualifications also experienced growth in hours of work, reflecting increasing demand for skills. Nevertheless income returns to qualifications were higher for Māori than for non-Māori in both years. This reflects the particular and increasing disadvantage faced by Māori with no qualifications compared to Europeans with no qualifications and the fact that the gap between mean incomes of Māori and Europeans reduces as qualifications rise. Māori participation in higher education increased strongly over the period.

Controlling for a wide range of characteristics, Māori residing in rural areas are more disadvantaged than any other group. Māori are also less likely to be married. Not being married is associated with lower incomes for males.

By 1996 there was little difference among ethnic groups in access to managerial and professional occupations for people with higher educational qualifications. Overall, most of the ethnic gap in incomes can be explained by differences in the characteristics of the groups, rather than by differences in the way in which these characteristics are translated into income.

Acknowledgements

I would like to thank Dr. Ron Crawford at the New Zealand Treasury, and Drs. Simon Chapple and Dave Maré then at the Department of Labour, and external referees for the Treasury for insightful comments on the study; Peter McMillan and Theva Thevathasan at Statistics New Zealand for providing the data sets and assistance with data processing; and Adam Warner and Calvin Chan at the University of Auckland for research assistance. None of the above individuals or organizations is, of course, responsible for the views expressed.

Disclaimer

The views expressed in this Working Paper are those of the author and do not necessarily reflect the views of the New Zealand Treasury. The paper is presented not as policy, but with a view to inform and stimulate wider debate.

1  Introduction

This study investigates the relationship between educational qualifications and income in New Zealand over the period 1981 to 1996. Reducing disparities between Māori and non-Māori New Zealanders in average income is an important and ongoing focus of government policy. This study builds on Maani (2000) by looking at how ethnic differences in the relationship in 1986 and 1996 are mediated by occupation, industry, hours of work, and locality[1].

Over the decade since the mid-1980s those with higher education, skills and training have had a relative increase in employment opportunities, reflected in higher employment rates in occupations and industries which have experienced growth, and higher hours of work and hourly wages. In particular, Māori have been more heavily concentrated in elementary and low skilled jobs, which did not experience growth to the extent that professional and other skill-based occupations have in the past decade[2]. In addition, other studies (Chapple and Rea, 1999) suggest that Māori in rural locations are more disadvantaged in employment outcomes than any other group.

Maani (2000) found that in both 1986 and 1996, Māori had lower qualifications than non-Māori and that, for given qualifications; Māori incomes were lower than non-Māori. However, returns to education were higher for Māori than non-Māori. This study investigates the extent to which these differences are associated with differences in occupation, industry, hours of work, and locality, focussing on all employed persons. It also decomposes the mean income gap across ethnic groups into effects due to differences in the characteristics of the groups, and effects due to differences in the way in which these characteristics are translated into income (after Oaxaca, 1973; and Oaxaca and Ransom, 1999).

The remainder of this paper is organised as follows. The next section provides a description of the data, and group characteristics. Section 3 sets out the methodology for estimation of rates of return to education. Section 4 reports results of these. Section 5 reports decompositions of ethnic differences in income. Section 6 concludes.

Notes

  • [1]This work is part of a vast and growing international literature on the effects of educational investments on income (see for example, Miller (1982), Chapman (1988), McNabb and Richardson (1989) for Australia; Hunt and Hicks (1985), Maani (1996, 1997, 1999 and 2000), Gibson (1988) and Dixon (1988) for New Zealand). The analysis of changes in rates of return to education over time has been pursued by Miller (1984), Chia (1991), and Gregory (1996) for Australia. Borland (1999) has provided a recent and comprehensive survey of analyses on changes in the income distribution in Australia and the contribution of educational attainment and earnings to it. For New Zealand Maani (1997, 1999) has examined the returns to post-compulsory education across four census years; and Ryoo (1988), Behrman and Birdsall (1987), Wilson (1985), and Psacharopoulos (1994) provide international evidence on this question.
  • [2]Winkelmann and Winkelmann (1998). Neville and Saunders (1988) investigate similar effects of ethnic differences in occupation and industry in Australia.. Hertzog (1997) also investigates ethnic differences in employment outcomes in New Zealand, and finds them to be associated with differences in the rate of involuntary job separations.

2  Data and group characteristics 

This study uses a 50% sample of individual level data from the 1986 and 1996 New Zealand Census of Population and Dwellings[3]. This allows analysis of changes in relative educational attainment and income levels over a decade of significant economic and policy change. Important for comparisons across time, both 1986 and 1996 were non-recession years with comparable levels of economic activity.

While the focus of the study is on Māori and Non-Māori comparisons, four ethnic categories of ‘Māori’, ‘Part-Māori’, ‘European’ and ‘Other’ are considered separately, where Māori refers to those who identify solely with the Māori ethnicity, and ‘Part-Māori’ refers to those who identify with both Māori and at least another ethnicity. ‘Other’ includes Pacific Island ethnicity, and also other non-European non-Māori immigrants. Immigration patterns since 1991 suggest that, relative to 1986, returns to higher education in 1996 for the ‘Other Ethnic’ return are likely to have been adversely affected by language and other barriers (see for example, Maani, 1999 and Winklemann and Winklemann, 1998). It is thus useful to separate this group in analyses from the ‘European’ ethnic group.

Table 1a and 1b show sample characteristics for males and females, respectively. In both 1996 and 1986 a larger proportion of the “all employed” Māori population had no school qualifications. A notably smaller proportion of the employed Māori labour force was engaged in ‘Managerial and Professional Occupations’. Māori males worked slightly fewer hours per week than any other group in 1986, and fewer than all except the ‘Other’ group in 1996. Māori were less likely than any other group to live in a major urban area. They were also less likely to be married than the European or ‘Other’ ethnic group.

Table 1a – Sample characteristics: employed males
  Māori Part-Māori European Other
  1986 1996 1986 1996 1986 1996 1986 1996
Hours worked (per week) 42.78 42.86 44.46 45.14 45.69 46.32 43.63 42.52
(-12.31) (-14.78) (-12.04) (-14.8) (-11.91) (-14.3) (-12.17) (-14.79)
% No qualifications 61.19 56.39 39.92 33.17 30.54 25.75 44.80 34.10
% Managerial/administrative 1.11 4.70 3.52 9.81 8.00 15.81 2.63 11.73
% Professional 4.11 4.73 10.39 7.17 14.95 12.08 11.82 13.14
% Married 46.55 41.92 49.38 43.34 62.2 58.32 58.02 57.83
% Major urban 59.82 63.18 63.73 67.03 68.13 68.23 88.19 91.95
% Semi-urban 22.00 20.88 19.83 17.64 15.03 14.76 7.36 5.42
Sample size 28,660 18,220 8174 17,129 335,633 281,247 17,274 19,931

Standard deviations in parentheses

Table 1b   – Sample characteristics: employed females
  Māori Part-Māori European Other
  1986 1996 1986 1996 1986 1996 1986 1986
Hours worked (per week) 36.25 34.55 36.36 34.78 34.45 33.81 37.46 35.99
(-12.78) (-15.88) (-13.17) (-16.15) (-14.39) (-16.09) (-13.1) (-15.44)
% No qualifications 59.02 48.13 38.72 27.34 32.66 22.87 43.43 30.99
% Managerial/administrative 0.65 5.69 1.58 7.97 2.37 10.16 0.95 7.95
% Professional 10.14 11.21 15.8 12.84 20.59 17.7 11.79 14.63
% Married 47.04 41.83 45.52 42.87 59.74 37.4 56.42 55.5
% Major urban 65.36 65.90 69.51 69.01 72.10 70.93 90.60 92.57
% Semi-urban 19.85 19.43 17.04 17.02 13.84 14.10 5.80 4.98
Sample size 16,899 13,242 6,008 15,137 228,524 234,080 12,032 16,157

Standard deviations in parentheses

A more comprehensive set of sample characteristics is provided in Appendix Tables A.1 and A.2 for males and females, respectively[4].

Figures 1 – 3 show the relationship between qualification levels and income (Figure 1), being in the managerial and professional occupations (Figure 2), and weekly hours worked per week (Figure 3), by ethnic group and gender in both 1986 and 1996. Tables corresponding to these figures are in Appendix B.

Figure 1 shows that for most qualification levels, the income gap (defined as the difference in mean annual gross income) between employed Māori and European males widened over the decade, as noted in Maani (2000). For females, differences among ethnic groups in average incomes for each qualification level were relatively small in both years, except for the lower incomes of members of the ‘Other’ ethnic group with university qualifications in 1996.

Figure 2 represents the proportion of the employed population engaged in managerial and professional occupations. The difference across ethnic groups for males with higher education has decreased significantly between 1986 and 1996. For Māori, Part-Māori and European females the difference had virtually disappeared by 1996, signalling the importance of educational attainment as a means of access to professional and managerial occupations.

Particularly for females, those with higher qualifications tended to work more hours than others in both years (see Figure 3). In addition, those with higher qualifications worked more hours in 1996 than in 1986 – consistent with the increased demand for skills noted earlier. As Māori are more concentrated in groups with lower qualifications, lower hours of work is clearly one explanation both of their lower incomes, and also of the increase in their relative disadvantage over time.

Figure 1- Income by highest educational qualification and ethnicity

 

Figure 1- Income by highest educational qualification and ethnicity: All employed Males 1986.

 

Figure 1- Income by highest educational qualification and ethnicity: All employed Males 1996.

 

Figure 1- Income by highest educational qualification and ethnicity: All employed females 1986.

 

Figure 1- Income by highest educational qualification and ethnicity: All employed females 1996.
 

 

Notes

  • [3]Compared to the 20% sample in Maani (2000), this allows more precise estimates for small subgroups such as female Māori with post-graduate qualifications.
  • [4]The means reported in these tables differ from those in the corresponding tables in Maani (2000) because the latter are for the whole working age population, including those unemployed and out of the labour force, while, in the current study, they cover only the employed population. There are also small differences due to the extra control variables used in the regressions in the current study. The sample is restricted to those with observations on all variables included in the regressions.

2  Data and group characteristics (continued)

Figure 2 - Managerial and professional occupations by highest educational qualification and ethnicity

 

Figure 2 - Managerial and professional occupations by highest educational qualification and ethnicity: All Employed Males 1986.

 

Figure 2 - Managerial and professional occupations by highest educational qualification and ethnicity: All Employed Males 1996.

 

Figure 2 - Managerial and professional occupations by highest educational qualification and ethnicity: All Employed Females 1986.

 

Figure 2 - Managerial and professional occupations by highest educational qualification and ethnicity: All Employed Females 1996.
 

Figure 3 - Hours worked per week by highest educational qualification and ethnicity

 

Figure 3 - Hours worked per week by highest educational qualification and ethnicity: All Employed Males 1986.

 

Figure 3 - Hours worked per week by highest educational qualification and ethnicity: All Employed Males 1996.

 

Figure 3 - Hours worked per week by highest educational qualification and ethnicity: All Employed Females 1986.

 

Figure 3 - Hours worked per week by highest educational qualification and ethnicity: All Employed Females 1996.
 

Moreover, Figure 3 and Table 1 show that, while in 1986 there was little difference amongst ethnic groups in the hours worked by males, by 1996 European males in all but the highest qualification group, worked more hours than Māori – on average 3.5 hours more per week, equivalent to almost 22 extra 8 hour days of employment per year. In contrast, it may be noted from Figure 3 that Māori females worked more hours per week on average, than other groups.

The analyses in this section have highlighted a few important points. First, there are significant differences in the educational attainment and occupations of Māori and Non-Māori groups. Second, once educational attainment is accounted for (as in Figure 2) much of the occupational difference disappears, particularly in 1996. Third, weekly hours of work, locality of residence and the proportion married vary across ethnic groups, in ways that may help explain differences in income.

3  The specification

The following specification was estimated:

(1)    ln Yik= ak + Σbjk X ijk + Σcjk Z ijk + d1k Nik + d2 k Nik2 + vik

where the dependent variable is the natural logarithm of annual income in current dollars, k stands for each of the four ethnic groups. X ijk represent the six binary educational qualifications variables for individual i, where the excluded educational qualification level is ‘no school qualifications’. The explanatory variables, Z ijk, control for occupation[5] and industry[6] (18 one digit variables), weekly hours of work, marital status (‘married’ or ‘de facto’ versus other categories), and locality of residence (‘major urban’ and ‘rural’ versus ‘semi-urban’). Variable N measures the potential years of work experience by educational qualification level, in the usual quadratic form.[7]

Equation (1) is estimated for both 1986 and 1996, and all models estimated utilise the White adjustment to correct for heteroscedasticity and for consistent estimates of coefficient variances. The model incorporates before-tax income levels.[8] The model is unrestricted by ethnicity, gender, and for each year.

While the expected sign for ‘hours of work’ is positive, the effects of ‘locality of residence’ and ‘marital status’ are a-priori not entirely clear, and examining their effect is of interest, in particular across ethnic groups. In addition, ‘urban’ living is expected to have a positive coefficient if ‘urban’ job and employment opportunities are greater than when living in ‘rural’ or ‘semi-urban’ areas, but the extent of it may very well vary for the Māori population. Finally, there is wide empirical evidence of a positive relationship for males between being married and income, which is likely to represent mainly supply side effects.

Notes

  • [5]Nine one digit ‘occupation’ and nine ‘industry’ categories were controlled for. The 1996 and 1986 census occupation classifications were somewhat different and therefore different specifications for the two years are chosen. The occupation categories in 1986 were: (1) Managerial/administrative, (2) Professional, (3) Clerical, (4) Service, (5) Agricultural, (6) Production/transport workers, and (7) Sales. In 1996 the categories were: (1) Managerial/administrative, (2) Professional, (3) Clerical, (4) Service, (5) Agricultural, (6) Trade oriented, (7) Plant and machine operator, (8) Elementary/low-skilled, and (9) Technical. Importantly, the base occupation category, which is ‘Clerical’ is used across the two sample years in 1986 and 1996.
  • [6]The industry categories in both years were: (1) Agriculture, hunting, forestry, fishing, (2) Mining and quarrying, (3) Manufacturing, (4) Electricity, gas, and water, (5) Construction, (6) Wholesale and retail trade, restaurants and hotels, (7) Transport, communication, (8) Business and financial services, (9) Community, social and personal services.
  • [7]Years of experience is specified as ‘age - years of schooling - 5’ since school starts at age 5 in New Zealand.
  • [8]The income information in the New Zealand census (and similar to Australia) is reported in 13 categories, based on an annual gross income. The mid-point of these categories has been used as a measure of income throughout the study. The lowest income category in the census is nil income or loss for which income of zero is designated. The rest of the annual categories were $2,500 or less, $2,501-$5,000, up to $100,000 or more, for which, based on a Statistics New Zealand estimate, a mid-point of $130,960 was assumed.

4  Results

Two versions of equation (1) were estimated (without and with hours of work, occupation and industry). The estimates of the second of these are summarised in Tables 2a-2d. Including hours of work, occupation and industry led to a significant decrease in the coefficients on education.[9] This supports the hypothesis that the positive effect of education on income is partly through improved access to better-paid occupations and industries, and higher hours of work. This effect is relatively stronger for the Māori and ‘Other’ ethnic groups than for the European and Part-Māori groups.[10]

It is interesting to note that while ‘hours of work’ has a positive effect for all ethnic groups in both years, this significantly increased in 1996 relative to 1986. The results in Tables 2a-2d also show that ‘locality’ has a statistically significant influence on ethnic income differentials. For example, Māori males residing in rural areas had the greatest disadvantage in income levels, compared to all other ethnic groups.

In addition, while European and Part-Māori males and all females had relatively higher income levels if residing in a major urban area and compared to a semi-urban area, Māori males did not show such an advantage (the same is true for the ‘Other’ ethnic group). This indicates that the relatively higher proportion of Māori males resident in semi-urban areas is consistent with income incentives as estimated in this study. By the same token, these results indicate that Māori males residing in ‘major urban’ and ‘rural’ areas were engaged in employment resulting in relatively lower income returns.[11]

Overall, the result of adding the new set of variables in the econometric estimations indicates that the link between educational attainment and income is partly through access to certain highly demanded occupations, and greater hours of work. It also shows that, other things equal, Māori males earn higher incomes in ‘semi urban’ locations (where Māori are most concentrated), and those living in rural locations are the most disadvantaged of any group.

Table 2a – Effects of education on incomes of Māori: 1986 and 1996
  Males Females
Intercept 1986 1996 1986 1996
8.9329 8.7321 8.1840 8.4494
(-326.95) (-225.61) (-175.73) (-168.81)
Highest qualification:        
School Certificate 0.0822 0.1296 0.0718 0.1266
(-8.63) (-9.31) (-4.88) (-7.59)
U.E./Sixth Form Cert. 0.1541 0.2497 0.1626 0.2644
(-9.32) (-13.77) (-7.75) (-12.90)
Bursary 0.1839 0.1936 0.0656 0.0885
(-5.05) (-6.70) (-1.28) (-2.52)
Diploma 0.2267 0.2364 0.2524 0.2703
(-29.60) (-16.69) (-16.15) (-14.42)
Bachelor's degree 0.3864 0.3482 0.4269 0.4903
(-8.21) (-8.15) (-7.48) (-12.46)
Postgraduate 0.4778 0.6497 0.3189 0.729
(-10.62) (-11.97) (-2.85) (-13.54)
Hours worked (per week) 0.0056 0.0105 0.0189 0.0141
(-19.17) (-25.94) (-31.74) (-31.54)
Married 0.1478 0.1740 -0.1436 -0.1069
(-23.09) (-17.29) (-12.25) (-8.45)
Major urban -0.0321 0.0004 0.0675 0.1043
(-4.38) (-0.04) (-5.21) (-6.99)
Rural -0.0933 -0.0945 -0.0545 -0.0495
(-9.44) (-9.62) (-2.83) (-2.37)
Experience 0.04275 0.06325 0.03044 0.05259
(-39.03) (-36.34) (-124.84) (-25.79)
Experience2 -0.00079 -0.00111 -0.00047 -0.00089
(-32.70) (-30.88) (-12.3) (-20.47)
F 358.07 275.86 213.81 203.44
Sample size 28,659 18,219 16,898 13,241

Dependent variable: the natural logarithm of annual income Specification includes one digit occupation and industry dummies. Least squares regression coefficients (t-ratios based on White consistent standard errors)

Table 2b – Effects of education on incomes of Part-Māori: 1986 and 1996
  Males Females
Intercept 1986 1996 1986 1996
8.8399 8.6738 7.9040 8.3508
(168.05) (215.38) (86.28) (152.28)
Highest qualification        
School Certificate 0.0690 0.1202 0.0659 0.0959
(4.09) (8.45) (2.64) (5.59)
U.E./Sixth Form Cert. 0.1258 0.1991 0.1585 0.1825
(5.33) (11.88) (4.98) (9.23)
Bursary 0.0731 0.0501 0.1521 0.0458
(1.69) (2.14) (2.63) (1.74)
Diploma 0.2422 0.2504 0.2308 0.2528
(18.02) (18.66) (8.26) (14.01)
Bachelor's degree 0.3926 0.4424 0.4490 0.3553
(10.31) (16.70) (7.24) (11.87)
Postgraduate 0.4444 0.5954 0.4159 0.5229
(7.04) (17.14) (5.81) (12.12)
Hours worked (per week) 0.0049 0.0111 0.0208 0.0170
(8.49) (26.75) (21.29) (36.93)
Married 0.1772 0.1502 -0.2083 -0.1480
(13.27) (14.17) (9.88) (11.90)
Major urban -0.0070 0.0580 0.0497 0.1488
(0.52) (4.67) (2.08) (9.67)
Rural -0.1028 -0.0726 -0.0476 0.0383
(5.11) (4.19) (1.33) (1.73)
Experience 0.04762 0.06839 0.04108 0.05432
(23.58) (39.24) (15.24) (28.94)
Experience2 -0.00089 -0.00128 -0.00073 -0.00095
(18.98) (32.74) (11.04) (21.95)
F 157.05 370.19 86.98 276.80
Sample size 8,173 17,128 6,007 15,136

Dependent variable: the natural logarithm of annual income Specification includes one digit occupation and industry dummies. Least squares regression coefficients (t-ratios based on White consistent standard errors)

Table 2c – Effects of education on incomes of Europeans: 1986 and 1996
  Males Females
Intercept 1986 1996 1986 1996
8.9331 8.8250 8.1418 8.5343
(1033.34) (836.09) (545.03) (592.14)
Highest qualification        
School Certificate 0.0473 0.0704 0.0620 0.0826
(15.59) (18.03) (14.27) (17.39)
U.E./Sixth Form Cert. 0.0840 0.1354 0.0925 0.1475
(22.35) (30.97) (16.88) (27.00)
Bursary 0.0249 -0.0434 0.0366 -0.0706
(3.63) (7.00) (3.53) (9.31)
Diploma 0.1784 0.1561 0.1614 0.1788
(83.42) (48.19) (36.55) (37.26)
Bachelor's degree 0.3303 0.3440 0.2832 0.2853
(67.55) (64.49) (32.81) (42.13)
Postgraduate 0.4077 0.4691 0.3688 0.4371
(74.45) (73.49) (35.34) (52.18)
Hours worked (per week) 0.0050 0.0107 0.0224 0.0206
(48.36) (97.38) (136.66) (165.75)
Married 0.1479 0.1443 -0.1983 -0.1677
(69.64) (55.07) (63.36) (55.60)
Major urban 0.0506 0.0847 0.0788 0.1208
(22.04) (27.55) (19.16) (29.61)
Rural -0.0763 -0.0356 -0.0423 0.0378
(21.76) (8.08) (6.78) (6.43)
Experience 0.04306 0.06117 0.02375 0.04219
(139.92) (149.89) (55.83) (89.15)
Experience2 -0.00077 -0.00111 -0.00032 -0.00068
(120.46) (131.88) (33.42) (65.94)
F 6574.08 6229.73 3687.49 4764.87
Sample size 335,632 281,246 228,523 234,079

Dependent variable: the natural logarithm of annual income Specification includes one digit occupation and industry dummies. Least squares regression coefficients (t-ratios based on White consistent standard errors)

Notes

  • [9]Incremental F tests based on the two specifications imply that the combined effect of the added variables is statistically significant, with P values smaller than 0.01.
  • [10]The usual relevant adjustments are made to interpret the coefficients as a percentage gain in income in relation to dichotomous (binary) variables for educational qualifications, given the semi-logarithmic functional forms of the ‘earnings functions’ (see e.g. Halvorsen and Palmquist, 1980). For example, the percentage gain in income from an education level is derived as: gj = [ exp (bj) - 1 ] times 100, where gj reflects the percentage gain relating to this education level, and bj is the regression coefficient..
  • [11]It is interesting that for Australia, Daly (1993) did not find a significant difference in the probability of employment for rural and urban Aborigines, which she expected to reflect the effect of the Community Development Employment Project (CDEP) scheme in creating ‘employment’ in remote areas at the time of the 1986 Census.

4  Results (continued)

Table 2d – Effects of education on incomes of “Other” ethnic groups: 1986 and 1996
  Males Females
  1986 1996 1986 1996
Intercept 8.8431 8.6581 8.2951 8.3268
(188.35) (162.12) (106.71) (115.63)
Highest qualification        
School Certificate 0.0495 0.1820 0.0547 0.1973
(3.52) (9.85) (3.14) (10.16)
U.E./Sixth Form Cert. 0.1334 0.2640 0.1292 0.2407
(7.17) (13.73) (5.66) (10.96)
Bursary -0.0247 0.0545 -0.0320 -0.0210
(0.84) (2.29) (0.78) (0.79)
Diploma 0.1937 0.2508 0.1308 0.1696
(17.21) (16.08) (6.95) (8.73)
Bachelor's degree 0.3235 0.2136 0.2175 0.2103
(13.38) (10.38) (5.70) (9.03)
Postgraduate 0.4154 0.3790 0.3163 0.3488
(13.96) (14.69) (5.71) (10.57)
Hours worked (per week) 0.0091 0.0127 0.0198 0.0179
(19.18) (29.20) (30.02) (37.67)
Married 0.0815 0.0511 -0.1047 -0.1212
(8.08) (4.30) (7.83) (9.72)
Major urban -0.1050 -0.0702 0.0642 0.0832
(6.51) (3.26) (2.38) (3.34)
Rural -0.0937 -0.0002 -0.0615 0.0309
(2.94) (0.00) (1.27) (0.63)
Experience 0.03599 0.05819 0.02733 0.04930
(22.66) (31.01) (13.29) (24.39)
Experience2 -0.00061 -0.00101 -0.00041 -0.00081
(17.42) (24.59) (8.47) (17.82)
F 221.60 350.78 126.78 274.08
Sample size 17,273 19,930 12,031 16,156

Dependent variable: the natural logarithm of annual income Specification includes one digit occupation and industry dummies. Least squares regression coefficients (t-ratios based on White consistent standard errors)

5   Decompositions

The analyses in the previous sections have established the role of hours of work, occupation, industry and locality in explaining the positive relationship between educational qualifications and income, and thereby the income gap across ethnic groups. An important question for strategies toward reducing the income gap is the extent to which lower educational attainment, or alternatively, potential difficulties for Māori in translating qualifications to income returns can explain the income gap and its increase between 1986 and 1996. This question is addressed by using the estimates of equation 1 (specification 2 in Tables 2a-2d) to decompose the income gap into a component due to differences in the characteristics (such as qualifications) of ethnic groups, and a component due to ethnic differences in the way that these characteristics are translated into income (Oaxaca, 1973). Comparisons are with the European group:

(2)      

 

 

where ‘E’ denotes European, M Māori, (and subscript ‘P’ may be substituted for M for Part-Māori, and ‘O’ for the ‘Other’ ethnic group depending on the comparison), k the number of explanatory variables,

 

regression coefficients,

 

mean characteristics,

 

 

 

is the differential in log incomes due to the difference in intercepts,

 

is the differential explained by differences in mean personal characteristics, and

 

 

 

is the income differential explained by coefficients.

 

The above formulation uses the coefficients from the ‘European’ equations as the weight for the effect of differences in the mean ‘characteristic’ of each ethnic group relative to the European group. This has the advantage of using the same set of base coefficients for the decompositions across ethnic groups. However, the choice of the base group for coefficients and mean characteristics can affect the results. In particular, when coefficients (or returns to characteristics) are greater for the European sample, this specification tends to estimate slightly larger ‘characteristic’ effects than when using alternative weights, in which the base groups are reversed. Results from these alternative specifications are compared in Appendix D. The weighting choice does not change any of the conclusions of the Māori, non-Māori decomposition analyses[12].

A second factor relating to the interpretation of the decomposition results when sets of binary variables are present (e.g. for education, occupation, locality, and industry) is that the intercept effects are not invariant to the choice of each base category. However, the sum of the coefficient effects (the sum of the intercept and other variable coefficient effects) is invariant (Oaxaca and Ransom, 1999). Therefore, in Table 3, the sum of ‘the coefficient effects’ is provided for comparison to the effect of ‘characteristics’.

Table 3 – Decompositions of log income differentials - employed males and females
Log income differential explained by: 1986 1996
 

 

 

 

 

 

 

Males
Overall difference 0.25064 0.16121 0.22514 0.35388 0.23317 0.31301
Intercept differential 0.00026 0.09320 0.09003 0.09290 0.15122 0.16687
Effect of characteristics 0.18199 0.14181 0.04331 0.23184 0.18064 0.04812
 

 

(72.6%) (88.0%) (19.2%) (65.5%) (77.5%) (15.4%)
Effect of coefficients (intercept+ coefficients) 0.06865 0.0194 0.18183 0.12204 0.05253 0.26489
 ( b0E – b0M/P/O )+

 

(27.4%) (12.0%) (80.8%) (34.5%) (22.5%) (84.6%)
Females
Overall difference 0.09795 0.04198 0.00642 0.15531 0.10585 0.08972
Intercept differential -0.04226 0.23776 -0.15329 0.08485 0.18352 0.20747
Effect of characteristics 0.09007 0.02963 -0.00510 0.11610 0.08367 0.02149
 

 

(92.0%) (70.6%) (-79.5%) (74.7%) (79.0%) (24.0%)
Effect of coefficients (Intercept+ coefficients) 0.00788 0.01235 0.01152 0.03921 0.02218 0.06823

Oaxaca Method, Based on the specification 2 in Tables 2a-2d. Percentage contributions to the overall difference in the log income differential are in parentheses.

Table 3 shows both the mean income differentials by ethnicity in 1986 and 1996, and the decomposition of these differentials into a component due to ethnic differences in characteristics (‘Effect of characteristics’), and a component due to ethnic differences in translating these characteristics into income (‘Effect of coefficients’). The first row for both males and females shows the generally smaller gap for the Part-Māori group, and the increase in the gap for all three ethnic groups between 1986 and 1996.

First, a major result of the decomposition analyses is that the majority proportion of the Māori-European income differential for both males and females (65.5% of the differential for males and 74.7% of the differential for females in 1996) can be explained by their higher educational qualifications and differences in other control variables. These results support the hypothesis that, among those employed, differences in education, occupation, hours of work and other work related characteristics make a stronger contribution to differences in income between Māori and European, than do differences in translating similar characteristics into income.

Table 3 also shows that intercept effects made a greater contribution in 1996 compared to 1986 in explaining Māori relative income levels for both males and females. While these intercept effects are specific to the choice of the base categories in the model (e.g. in this case, ‘no school qualifications’, clerical occupation, ‘semi-urban’ locality, single, etc.), the comparisons across ethnic groups are relevant and they highlight the earlier finding that by 1996 those in the base category of no school qualifications were relatively more disadvantaged in the labour market, and that the effect was greater for Māori males in the base category of ‘no school qualifications’.

In addition, Table 3 shows that, for both male and female Part-Māori, income returns to characteristics were higher than for Māori or Other ethnic groups, and closer to those for the European group. Conversely, Other ethnic groups had the lowest income return to characteristics. This is likely to reflect the effect of language barriers and other factors negatively influencing the labour market outcomes of immigrants in this group.

The estimations and decompositions have highlighted the importance of differences in educational attainment in explaining why relative Māori income levels have deteriorated over the period 1986 to 1996. While Māori became more qualified over the period, so did other groups. This, combined with higher returns to educational qualifications meant that Māori had relatively lower incomes in 1996, compared to 1986. The analysis shows that educational qualifications have been exerting their influence on ethnic differences in income over the decade partly through differences in occupation and hours of works. Other factors such as locality have also influenced differentials over time. For example, while European males in major urban areas had significantly higher income levels than those residing in rural or semi-urban areas, Māori males did not face advantages in major cities, but had higher income returns in semi-urban areas. Finally, decompositions have shown that differences in group characteristics rather than returns to characteristics can explain the major part of the ethnic income differentials.

Notes

  • [12]A further alternative is the Neumark (1988) method that combines all samples. The Oaxaca method was preferred, because of the large number of observations for the four ethnic categories.

6  Conclusion

The results of the current study provide strong evidence on the contribution of educational attainment to the income gap, and its increase between 1986 and 1996.

This period has experienced major changes in the New Zealand economy leading to higher demand for skills. Given that a large proportion of the Māori population had left school without qualifications and was engaged in elementary occupations, Māori males, in particular, faced barriers in responding to the increased demand in high skill occupations. At the same time, elementary and low skilled jobs did not experience growth to the extent that professional and other skill-based occupations have in the decade. A related finding is the evidence that educational effects on differences in income have partly been mediated through differences in access to hours of work per week.

An important finding is that, while there are significant differences in educational attainment and occupations of Māori and Non-Māori groups, once educational attainment is controlled for, much of the gap in occupational status disappears – particularly in 1996. As a result, the income gap at the higher education levels has narrowed over the period.

The results also show that, other things being equal, Māori in rural areas face particular disadvantages. Further investigation of the reasons for this, and the possibilities for advanced education and employment outside of the urban and semi urban areas is therefore important. Conversely, the relative success of employed Māori residing in semi-urban areas, and further examination of the reasons for it is also of special interest.

The decomposition results highlight the importance of ‘characteristics’, such as education, hours of work and occupation, rather than differential returns to higher skills by ethnicity in explaining the widened income gap. Indeed, returns to educational investments for Māori were higher at every level of education. An implication is that for Māori without school qualifications there is a greater relative opportunity cost of not pursuing post-compulsory education. Consistent with this, Māori participation in post-compulsory education increased over the period. Nevertheless, the Māori population, which had a large proportion without school qualifications, was in a disadvantaged position and did not attain tertiary educational levels similar to the rest of the population.

The results of this study suggest that investing in higher education provides important options for the Māori population in reducing the income gap. This is supported by the findings throughout the study that the income gap based on educational attainment within the ethnic group is far greater than the income gap across the ethnic groups and when controlling for educational attainment.

Thus, it is of concern that in the past, and currently the Māori population has not and does not acquire school qualifications at the same rate as others. The results of the current study suggest that this should be a particular focus for further research and policy development.

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Appendix A    Sample characteristics

Table A.1 – sample characteristics – all employed males (means & standard deviations)
  Māori Part-Māori European Other
  1986 1996 1986 1996 1986 1996 1986 1996
Age 32.40 35.37 31.03 33.36 36.69 38.55 33.50 35.08
(11.47) (11.34) (11.34) (11.11) (12.64) (12.00) (11.01) (10.55)
Annual income 1986 dollars $16,039 $17,988 $21,190 $17,094
($7,601)   ($9,475)   ($11,447)   ($9,856)  
Annual income 1996 dollars $26,351 $26,366 $29,554 $30,496 $34,814 $38,458 $28,085 $29,907
($12,488) ($16,936) ($15,568) ($21,417) ($18,807) ($27,350) ($16,193) ($23,600)
No qualifications 61.19% 56.39% 39.92% 33.17% 30.54% 25.75% 44.80% 34.10%
School Certificate 11.63% 13.32% 14.03% 15.45% 12.19% 12.28% 12.75% 8.77%
U.E./Sixth Form Cert.(year 12) 4.34% 8.08% 8.44% 11.93% 8.39% 10.29% 7.09% 8.54%
Bursary (year 13) 1.13% 3.89% 3.40% 7.44% 2.75% 6.03% 3.91% 7.58%
Diploma 20.59% 15.47% 29.63% 24.74% 37.10% 30.92% 21.44% 16.48%
Bachelor's degree 0.69% 2.08% 2.81% 5.24% 5.46% 9.57% 6.25% 15.96%
Postgraduate qual. 0.43% 0.77% 1.76% 2.03% 3.58% 5.16% 3.77% 8.58%
Hours worked (per week) 42.78 42.86 44.46 45.14 45.69 46.32 43.63 42.52
(12.31) (14.78) (12.04) (14.80) (11.91) (14.30) (12.17) (14.79)
Managerial/administrative occupation 1.11% 4.70% 3.52% 9.81% 8.00% 15.81% 2.63% 11.73%
Professional 4.11% 4.73% 10.39% 7.17% 14.95% 12.08% 11.82% 13.14%
Clerical 4.83% 4.80% 7.47% 5.56% 8.29% 4.96% 6.85% 7.49%
Service 6.40% 9.12% 6.78% 11.14% 5.90% 7.93% 8.35% 11.63%
Agricultural 11.97% 11.66% 11.02% 10.20% 11.96% 11.54% 4.53% 3.75%
Product./transport workers 68.92% 55.03% 41.15% 59.10%
Sales 2.66% 5.79% 9.76% 6.72%
Trade orientated 12.20% 16.17% 16.85% 12.06%
Plant and machine operator 27.66% 17.41% 10.81% 15.59%
Elementary/low-skilled 19.35% 13.06% 7.73% 14.40%
Technical 5.80% 9.47% 12.30% 10.21%
Agriculture and fisheries industry 12.43% 12.85% 11.01% 11.03% 11.93% 11.73% 4.71% 3.87%
Mining 1.33% 0.76% 0.61% 0.54% 0.62% 0.47% 0.16% 0.10%
Manufacturing 32.90% 25.13% 28.58% 21.68% 23.08% 18.02% 43.34% 26.58%
Electricity 2.42% 0.89% 1.90% 0.79% 1.62% 0.84% 1.07% 0.54%
Construction 14.58% 11.38% 12.42% 11.02% 10.69% 9.91% 6.19% 5.15%
Wholesaling 8.60% 12.28% 13.20% 18.04% 17.68% 19.21% 16.68% 24.34%
Transportation 10.67% 9.26% 10.01% 7.43% 8.95% 6.76% 8.26% 6.74%
Finance 1.64% 5.08% 4.48% 8.55% 7.42% 12.54% 4.80% 11.85%
Social services 15.43% 22.37% 17.80% 20.92% 18.01% 20.51% 14.79% 20.83%
Married 46.55% 41.92% 49.38% 43.34% 62.20% 58.32% 58.02% 57.83%
Major urban 59.82% 63.18% 63.73% 67.03% 68.13% 68.23% 88.19% 91.95%
Semi-urban 22.00% 20.88% 19.83% 17.64% 15.03% 14.76% 7.36% 5.42%
Rural 17.68% 15.87% 15.93% 15.27% 16.52% 16.92% 3.66% 2.56%

Table A.2 – sample characteristics – all employed females
  Māori Part-Māori European Other
  1986 1996 1986 1996 1986 1996 1986 1996
Age 32.12 35.85 30.27 33.34 35.15 37.88 32.53 34.08
(11.32) (10.98) (11.06) (10.92) (12.10) (11.61) (10.84) (10.29)
Annual income 1986 dollars $10,886 $11,747 $12,560 $11,935
($5,897)   ($6,791)   ($7,762)   ($6,751)  
Annual income 1996 dollars $17,885 $19,376 $19,301 $20,973 $20,636 $23,773 $19,610 $21,692
($9,689) ($13,055) ($11,157) ($15,129) ($12,752) ($18,089) ($11,091) ($16,046)
No qualifications 59.02% 48.13% 38.72% 27.34% 32.66% 22.87% 43.43% 30.99%
School Certificate 15.90% 16.54% 19.17% 18.66% 17.44% 16.93% 16.19% 10.75%
U.E./Sixth Form Cert.(year 12) 6.40% 10.65% 11.63% 14.95% 10.63% 12.88% 8.61% 9.88%
Bursary (year 13) 1.04% 3.96% 2.30% 8.03% 2.30% 5.88% 3.31% 8.37%
Diploma 16.62% 17.02% 24.30% 23.80% 30.46% 28.41% 21.40% 19.25%
Bachelor's degree 0.70% 2.66% 2.53% 5.40% 4.04% 8.81% 4.95% 15.21%
Postgraduate qual. 0.32% 1.03% 1.35% 1.82% 2.47% 4.21% 2.12% 5.55%
Hours worked (per week) 36.25 34.55 36.36 34.78 34.45 33.81 37.46 35.99
(12.78) (15.88) (13.17) (16.15) (14.39) (16.09) (13.10) (15.44)
Managerial/administrative occup. 0.65% 5.69% 1.58% 7.97% 2.37% 10.16% 0.95% 7.95%
Professional 10.14% 11.21% 15.80% 12.84% 20.59% 17.70% 11.79% 14.63%
Clerical 20.89% 18.22% 30.96% 24.83% 34.80% 25.08% 25.23% 22.73%
Service 23.24% 20.99% 17.91% 21.58% 13.70% 17.95% 20.45% 19.53%
Agricultural 6.57% 6.27% 5.56% 5.50% 6.61% 6.42% 2.66% 2.15%
Product./transport workers 33.27% 18.73% 10.05% 30.87%
Sales 5.24% 9.47% 11.88% 8.06%
Trade orientated 1.64% 1.35% 1.24% 1.69%
Plant and machine operator 8.71% 4.99% 3.13% 9.14%
Elementary/low-skilled 17.60% 9.04% 5.63% 12.57%
Technical 9.66% 11.92% 12.70% 9.62%
Agriculture and fisheries industry 7.40% 7.08% 6.51% 6.02% 7.63% 7.01% 3.24% 2.36%
Mining 0.16% 0.07% 0.23% 0.10% 0.18% 0.07% 0.10% 0.01%
Manufacturing 28.42% 15.47% 18.96% 10.87% 13.89% 8.83% 30.49% 17.15%
Electricity 0.18% 0.21% 0.35% 0.25% 0.34% 0.29% 0.18% 0.32%
Construction 1.24% 1.05% 1.60% 1.49% 1.78% 1.75% 0.60% 0.58%
Wholesaling 17.72% 18.74% 22.60% 24.04% 23.76% 22.37% 21.09% 24.97%
Transportation 7.81% 5.01% 7.27% 4.84% 5.32% 3.90% 6.03% 4.60%
Finance 4.08% 7.32% 8.36% 12.45% 11.25% 15.20% 8.08% 15.05%
Social services 33.00% 45.05% 34.12% 39.94% 35.86% 40.59% 30.19% 34.96%
Married 47.04% 41.83% 45.52% 42.87% 59.74% 57.40% 56.42% 55.50%
Major urban 65.36% 65.90% 69.51% 69.01% 72.10% 70.93% 90.60% 92.57%
Semi-urban 19.85% 19.43% 17.04% 17.02% 13.84% 14.10% 5.80% 4.98%
Rural 14.56% 14.66% 13.25% 13.96% 13.89% 14.94% 3.41% 2.41%
Sample size 16,899 13,242 6,008 15,137 228,524 234,080 12,032 16,157

Standard deviations in parentheses

Appendix B  Sample characteristics by Qualifications

Table B.1 Income and other employment characteristics by highest educational qualification of all employed Māori males in 1986

Table B.1 Income and other employment characteristics by highest educational qualification of all employed Māori males in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 33.28 28.90 26.66 28.02 33.16 33.01 37.02
Annual income 1986 dollars $15,053 $15,328 $16,197 $17,801 $18,577 $23,525 $26,606
($6,977) ($7,262) ($8,326) ($9,892) ($8,037) ($13,354) ($13,821)
Annual income 1996 dollars $24,732 $25,184 $26,611 $29,246 $30,522 $38,651 $43,712
($11,463) ($11,932) ($13,679) ($16,252) ($13,204) ($21,939) ($22,707)
Hours worked (per week) 41.94 43.34 44.32 43.36 44.60 43.25 49.13
(12.40) (12.57) (11.89) (12.12) (11.78) (13.04) (14.20)
Managerial/administrative occupation 0.71% 0.93% 2.64% 2.77% 1.72% 5.97% 8.20%
Professional 1.37% 2.85% 7.20% 9.85% 9.40% 51.74% 51.64%
Clerical 3.07% 7.46% 20.56% 26.46% 3.75% 11.44% 6.56%
Service 4.85% 10.07% 12.16% 15.38% 7.20% 5.97% 7.38%
Agricultural 13.85% 12.25% 8.72% 6.15% 8.10% 2.99% 8.20%
Product./transport workers 74.23% 61.65% 42.72% 35.08% 67.06% 16.92% 13.11%
Sales 1.92% 4.79% 6.00% 4.31% 2.77% 4.98% 4.92%
Agriculture and fisheries industry 13.98% 12.97% 9.84% 6.77% 9.18% 5.47% 9.02%
Mining 1.51% 0.99% 0.56% 0.62% 1.20% 1.00% 0.82%
Manufacturing 35.94% 33.01% 24.64% 25.54% 26.93% 10.45% 4.92%
Electricity 2.08% 1.44% 1.60% 1.85% 4.29% 0.50% 0.00%
Construction 14.97% 11.23% 7.20% 4.00% 17.99% 3.98% 3.28%
Wholesaling 7.35% 10.64% 12.88% 9.23% 10.29% 10.45% 8.20%
Transportation 10.67% 10.64% 11.04% 11.08% 10.75% 6.97% 4.10%
Finance 0.88% 2.40% 6.72% 7.38% 1.49% 13.43% 10.66%
Social services 12.62% 16.69% 25.52% 33.54% 17.88% 47.76% 59.02%
Married 47.24% 35.08% 32.88% 34.77% 54.21% 51.24% 60.66%
Major urban 57.79% 61.23% 68.56% 71.08% 61.65% 71.14% 72.13%
Semi-urban 22.66% 21.57% 16.88% 17.85% 22.11% 11.94% 9.84%
Rural 19.08% 16.54% 13.92% 10.15% 15.84% 15.92% 16.39%
Sample size 17,577 3,338 1,250 325 5,916 201 122

Standard deviations in parentheses

Table B.2 Income and other employment characteristics by highest educational qualification of all employed Māori females in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 33.68 28.70 25.20 29.03 32.83 30.38 34.98
(11.46) (10.09) (8.35) (11.07) (11.38) (9.09) (9.35)
Annual income 1986 dollars $9,836 $10,810 $12,045 $11,533 $13,651 $17,706 $19,586
($5,083) ($5,701) ($5,986) ($6,621) ($6,993) ($7,882) ($13,982)
Annual income 1996 dollars $16,160 $17,761 $19,789 $18,948 $22,428 $29,090 $32,179
($8,351) ($9,367) ($9,835) ($10,878) ($11,489) ($12,951) ($22,972)
Hours worked (per week) 35.51 36.54 38.27 36.45 37.54 41.83 42.63
(12.92) (12.23) (11.47) (11.96) (13.04) (12.01) (17.02)
Managerial/administrative occupation 0.46% 0.63% 1.48% 2.27% 0.78% 1.69% 3.70%
Professional 3.75% 4.60% 8.96% 9.66% 34.97% 76.27% 55.56%
Clerical 12.40% 33.23% 51.43% 46.59% 26.11% 12.71% 22.22%
Service 27.80% 21.18% 15.24% 18.18% 13.45% 1.69% 9.26%
Agricultural 8.01% 5.90% 3.32% 2.84% 4.06% 1.69% 3.70%
Product./transport workers 42.58% 27.93% 13.11% 14.77% 16.12% 1.69% 3.70%
Sales 5.01% 6.53% 6.46% 5.68% 4.52% 4.24% 1.85%
Agriculture and fisheries industry 8.66% 6.86% 4.43% 2.84% 5.27% 2.54% 7.41%
Mining 0.17% 0.07% 0.18% 0.00% 0.21% 0.00% 0.00%
Manufacturing 35.32% 24.89% 15.24% 17.05% 14.41% 5.08% 1.85%
Electricity 0.15% 0.22% 0.28% 0.00% 0.21% 0.00% 0.00%
Construction 1.13% 1.48% 0.92% 0.57% 1.53% 0.85% 1.85%
Wholesaling 18.52% 21.62% 17.17% 14.20% 12.27% 6.78% 7.41%
Transportation 6.92% 10.27% 12.10% 8.52% 7.29% 0.85% 5.56%
Finance 1.95% 7.12% 11.36% 13.64% 5.09% 9.32% 7.41%
Social services 27.18% 27.45% 38.32% 43.18% 53.72% 74.58% 68.52%
Married 50.88% 40.21% 28.81% 38.07% 47.95% 35.59% 51.85%
Major urban 64.46% 66.14% 70.45% 76.14% 64.57% 76.27% 70.37%
Semi-urban 20.67% 19.29% 17.73% 9.66% 19.35% 11.02% 16.67%
Rural 14.66% 14.47% 11.63% 12.50% 15.76% 11.86% 12.96%
Sample size 9,986 2,696 1,083 176 2,811 118 54

Standard deviations in parentheses

Table B.3 Income and other employment characteristics by highest educational qualification of all employed Part-Māori males in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 31.67 26.86 26.00 27.70 33.43 33.07 37.76
(12.15) (9.83) (8.97) (10.09) (10.72) (9.07) (10.12)
Annual income 1986 dollars $15,955 $15,699 $16,567 $17,280 $20,506 $26,726 $30,651
($8,043) ($8,207) ($9,064) ($11,246) ($9,414) ($12,983) ($14,462)
Annual income 1996 dollars $26,213 $25,793 $27,219 $28,390 $33,691 $43,910 $50,358
($13,215) ($13,484) ($14,892) ($18,477) ($15,467) ($21,330) ($23,761)
Hours worked (per week) 43.80 44.38 43.67 43.44 45.68 44.87 47.25
(12.44) (11.77) (10.93) (13.90) (11.65) (12.59) (13.67)
Managerial/administrative occupation 2.23% 2.69% 3.32% 5.04% 4.89% 7.83% 8.28%
Professional 1.71% 3.65% 10.98% 11.87% 15.32% 70.87% 73.10%
Clerical 4.56% 9.21% 24.71% 20.86% 4.39% 5.65% 4.83%
Service 5.47% 8.51% 11.13% 11.87% 6.53% 3.04% 2.07%
Agricultural 14.07% 11.99% 8.09% 10.79% 9.08% 1.74% 4.83%
Product./transport workers 67.22% 56.65% 33.38% 28.06% 54.37% 6.96% 3.45%
Sales 4.74% 7.30% 8.38% 11.51% 5.42% 3.91% 3.45%
Agriculture and fisheries industry 13.58% 11.82% 8.67% 10.07% 9.49% 2.17% 7.59%
Mining 0.92% 0.35% 0.29% 0.00% 0.53% 0.43% 0.00%
Manufacturing 36.15% 30.15% 20.81% 15.83% 23.49% 12.61% 13.10%
Electricity 1.38% 1.74% 1.30% 2.16% 3.04% 0.43% 0.00%
Construction 12.42% 11.12% 7.66% 6.47% 16.59% 2.61% 1.38%
Wholesaling 12.08% 15.55% 15.17% 16.91% 13.68% 8.26% 4.83%
Transportation 10.00% 10.51% 10.26% 11.51% 10.51% 3.91% 2.07%
Finance 1.50% 3.48% 10.40% 13.67% 3.37% 27.39% 15.17%
Social services 11.99% 15.29% 25.43% 23.38% 19.30% 42.17% 55.86%
Married 48.81% 39.18% 33.09% 34.89% 59.30% 58.26% 69.66%
Major urban 60.64% 63.42% 69.51% 76.62% 62.83% 76.09% 74.48%
Semi-urban 21.41% 19.81% 17.05% 10.07% 20.41% 14.35% 13.79%
Rural 17.40% 16.68% 13.01% 12.23% 16.18% 8.70% 11.03%
Sample size 3,270 1,151 692 278 2,435 230 145

Standard deviations in parentheses

Appendix B (continued)

Table B.4 Income and other employment characteristics by highest educational qualification of all employed Part-Māori females in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 32.73 27.40 24.57 25.20 31.66 31.49 32.65
Annual income 1986 dollars $9,978 $10,869 $12,158 $12,077 $13,861 $19,104 $17,986
Annual income 1996 dollars $16,394 $17,857 $19,975 $19,842 $22,773 $31,387 $29,551
($9,047) ($9,888) ($10,333) ($12,172) ($12,386) ($15,343) ($16,485)
Hours worked (per week) 35.12 36.35 38.06 37.71 36.99 39.18 39.73
(13.56) (12.25) (11.57) (15.15) (13.33) (15.95) (13.32)
Managerial/administrative occupation 1.07% 1.65% 1.86% 2.17% 1.57% 4.61% 7.41%
Professional 3.01% 4.51% 9.14% 9.42% 40.12% 73.03% 65.43%
Clerical 18.82% 42.63% 56.71% 52.90% 29.25% 12.50% 17.28%
Service 26.47% 16.46% 11.57% 14.49% 11.14% 1.32% 4.94%
Agricultural 6.96% 5.20% 4.86% 2.90% 4.65% 4.61% 2.47%
Product./transport workers 32.70% 17.16% 7.86% 10.14% 6.29% 2.63% 1.23%
Sales 10.96% 12.39% 8.00% 7.97% 6.97% 1.32% 1.23%
Agriculture and fisheries industry 7.65% 6.41% 6.00% 4.35% 5.81% 4.61% 2.47%
Mining 0.04% 0.26% 0.57% 0.00% 0.41% 0.00% 0.00%
Manufacturing 29.22% 18.89% 12.14% 13.04% 8.48% 5.92% 7.41%
Electricity 0.39% 0.17% 0.43% 1.45% 0.27% 0.66% 0.00%
Construction 1.68% 1.47% 1.71% 2.17% 1.64% 0.66% 0.00%
Wholesaling 27.29% 29.46% 18.00% 22.46% 14.49% 8.55% 3.70%
Transportation 6.66% 9.19% 11.57% 7.97% 5.40% 1.32% 3.70%
Finance 3.70% 11.18% 16.00% 18.12% 8.34% 13.82% 8.64%
Social services 23.38% 22.96% 33.57% 30.43% 55.16% 64.47% 74.07%
Married 51.74% 40.03% 30.00% 24.64% 49.62% 43.42% 46.91%
Major urban 68.07% 69.67% 71.00% 75.36% 68.76% 75.00% 83.95%
Semi-urban 18.74% 17.42% 16.29% 13.04% 15.99% 12.50% 6.17%
Rural 13.15% 12.74% 12.43% 11.59% 14.90% 11.18% 9.88%
Sample size 2,327 1,154 700 138 1,463 152 81

Standard deviations in parentheses

Table B.5 Income and other employment characteristics by highest educational qualification of all employed European males in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 39.05 32.27 30.70 30.44 37.94 35.46 40.14
(13.21) (12.61) (12.41) (11.72) (11.73) (10.44) (10.18)
Annual income 1986 dollars $17,999 $18,124 $19,110 $19,080 $22,513 $29,915 $33,466
($9,642) ($10,445) ($11,307) ($12,327) ($10,560) ($15,184) ($15,317)
Annual income 1996 dollars $29,572 $29,778 $31,397 $31,348 $36,988 $49,149 $54,984
($15,842) ($17,161) ($18,578) ($20,253) ($17,349) ($24,946) ($25,166)
Hours worked (per week) 46.27 45.92 44.53 42.80 45.98 45.19 46.37
(12.92) (11.58) (11.17) (13.56) (11.52) (11.57) (12.30)
Managerial/administrative occupation 5.24% 7.60% 10.13% 10.37% 8.81% 11.97% 10.62%
Professional 1.83% 4.53% 10.87% 16.37% 16.85% 65.90% 71.76%
Clerical 6.61% 11.31% 21.87% 20.91% 5.30% 5.18% 4.55%
Service 5.97% 6.78% 7.81% 9.88% 5.74% 1.99% 1.69%
Agricultural 18.93% 16.45% 12.68% 10.36% 7.91% 5.40% 3.62%
Product./transport workers 51.61% 40.56% 23.64% 18.32% 46.66% 3.95% 3.62%
Sales 9.81% 12.76% 12.99% 13.79% 8.73% 5.61% 4.15%
Agriculture and fisheries industry 18.30% 16.20% 12.77% 10.85% 8.13% 6.41% 4.64%
Mining 0.86% 0.50% 0.37% 0.29% 0.54% 0.55% 0.58%
Manufacturing 26.93% 23.27% 17.39% 16.36% 24.09% 12.54% 8.80%
Electricity 1.24% 0.92% 1.16% 0.82% 2.45% 1.27% 0.47%
Construction 11.07% 9.47% 6.25% 4.41% 14.00% 3.36% 2.01%
Wholesaling 18.18% 21.15% 19.75% 22.16% 17.38% 9.28% 6.72%
Transportation 10.31% 8.96% 8.98% 8.27% 9.19% 3.57% 1.71%
Finance 2.30% 6.67% 15.32% 16.28% 5.97% 27.07% 12.32%
Social services 10.80% 12.86% 18.01% 20.56% 18.26% 35.94% 62.75%
Married 64.01% 49.28% 43.65% 40.99% 69.56% 64.34% 74.82%
Major Urban 60.10% 64.62% 71.56% 77.81% 70.31% 79.27% 82.45%
Semi-urban 17.26% 14.61% 12.12% 9.33% 15.47% 10.08% 9.16%
Rural 22.36% 20.45% 15.92% 12.41% 13.90% 10.18% 8.13%
Sample size 103,697 41,372 28,405 9,314 125,299 18,411 12,059

Standard deviations in parentheses

Table B.6 Income and other employment characteristics by highest educational qualification of all employed European females in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 39.82 31.87 27.78 28.20 35.38 32.59 37.09
(11.70) (11.49) (10.93) (12.09) (11.48) (9.66) (10.21)
Annual income 1986 dollars $10,469 $11,641 $12,499 $11,695 $13,986 $17,528 $20,094
($6,683) ($6,877) ($6,885) ($7,619) ($7,989) ($10,072) ($11,539)
Annual income 1996 dollars $17,201 $19,126 $20,535 $19,214 $22,979 $28,799 $33,015
($10,980) ($11,299) ($11,313) ($12,519) ($13,125) ($16,548) ($18,958)
Hours Worked (per Week) 32.99 34.89 36.56 33.86 34.73 36.58 37.20
(14.93) (13.70) (12.44) (14.38) (14.71) (15.00) (15.18)
Managerial/administrative occupation 1.95% 2.60% 2.45% 2.33% 2.32% 4.37% 3.33%
Professional 2.80% 4.35% 9.88% 16.63% 42.34% 65.77% 76.62%
Clerical 29.67% 48.81% 56.84% 43.40% 28.12% 15.61% 10.51%
Service 22.07% 12.78% 9.02% 15.65% 8.99% 3.26% 2.58%
Agricultural 8.19% 7.45% 6.01% 5.57% 5.93% 3.90% 2.41%
Product./transport workers 18.52% 10.09% 5.95% 5.72% 4.39% 2.14% 1.50%
Sales 16.80% 13.93% 9.84% 10.70% 7.91% 4.95% 3.06%
Agriculture and fisheries industry 9.06% 8.52% 7.29% 6.25% 7.05% 4.72% 3.45%
Mining 0.13% 0.21% 0.25% 0.27% 0.21% 0.15% 0.12%
Manufacturing 21.06% 15.88% 11.69% 9.64% 7.87% 5.70% 4.51%
Electricity 0.32% 0.46% 0.62% 0.44% 0.21% 0.21% 0.11%
Construction 2.02% 2.31% 1.81% 1.44% 1.46% 0.81% 0.39%
Wholesaling 33.09% 29.25% 21.21% 25.02% 14.87% 8.76% 5.82%
Transportation 5.24% 6.86% 7.90% 6.02% 4.28% 2.14% 1.11%
Finance 6.22% 15.19% 23.28% 18.15% 9.50% 13.94% 7.34%
Social services 22.86% 21.32% 25.95% 32.77% 54.54% 63.58% 77.15%
Married 69.62% 55.90% 40.77% 34.12% 61.56% 51.82% 55.31%
Major urban 69.02% 70.76% 73.38% 80.13% 72.51% 80.60% 84.67%
Semi-urban 16.67% 14.56% 12.56% 8.98% 12.47% 8.34% 6.92%
Rural 14.23% 14.54% 13.83% 10.55% 14.85% 10.53% 8.10%
Sample size 75,041 39,991 24,373 5,279 69,846 9,261 5,654

Standard deviations in parentheses

Appendix B (continued)

Table B.7 Income and other employment characteristics by highest educational qualification of all employed Other males in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 35.10 29.72 28.12 29.29 34.27 33.54 37.60
(11.60) (10.00) (9.68) (9.44) (10.31) (9.54) (9.41)
Annual income 1986 dollars $14,667 $14,918 $16,117 $15,612 $18,795 $25,785 $31,082
($7,119) ($7,608) ($8,465) ($11,124) ($9,291) ($14,266) ($16,735)
Annual income 1996 dollars $24,097 $24,510 $26,480 $25,651 $30,879 $42,364 $51,066
($11,696) ($12,500) ($13,907) ($18,276) ($15,265) ($23,439) ($27,494)
Hours worked (per week) 42.98 43.84 44.28 41.73 44.65 43.72 46.40
(11.76) (11.76) (12.63) (15.40) (12.13) (12.39) (14.23)
Managerial/administrative occupation 0.88% 1.59% 3.41% 4.59% 3.66% 9.43% 6.29%
Professional 0.86% 2.72% 8.35% 10.50% 16.06% 62.20% 72.85%
Clerical 3.55% 8.79% 21.82% 15.53% 5.97% 8.32% 4.29%
Service 8.50% 9.51% 12.25% 13.61% 7.64% 3.42% 2.91%
Agricultural 5.45% 5.07% 5.19% 6.80% 3.50% 1.57% 1.69%
Product./transport workers 75.89% 63.09% 36.82% 38.02% 56.67% 7.86% 4.91%
Sales 4.87% 9.24% 12.17% 10.95% 6.51% 7.21% 7.06%
Agriculture and fisheries industry 5.37% 5.71% 5.52% 6.80% 3.60% 2.13% 2.91%
Mining 0.15% 0.14% 0.16% 0.15% 0.19% 0.18% 0.00%
Manufacturing 55.85% 44.88% 27.09% 32.84% 36.15% 18.39% 9.82%
Electricity 0.66% 0.77% 1.38% 0.59% 2.10% 0.92% 1.07%
Construction 6.35% 5.25% 3.65% 1.63% 9.76% 3.14% 2.30%
Wholesaling 14.54% 19.84% 23.11% 25.44% 17.00% 16.36% 11.04%
Transportation 7.52% 8.74% 11.27% 7.99% 10.92% 3.60% 1.84%
Finance 1.43% 2.99% 9.57% 9.02% 4.41% 21.26% 12.27%
Social services 8.13% 11.68% 18.25% 15.53% 15.87% 34.01% 58.74%
Married 60.29% 49.05% 40.23% 46.30% 62.61% 61.28% 75.15%
Major urban 88.82% 86.96% 87.51% 89.94% 85.91% 90.94% 90.95%
Semi-urban 7.30% 8.20% 7.38% 5.18% 7.77% 6.65% 6.13%
Rural 3.54% 4.08% 4.54% 2.66% 4.49% 2.13% 2.76%
Sample size 7,764 2,208 1,233 676 3,718 1,082 652

Standard deviations in parentheses

Table B.8 Income and other employment characteristics by highest educational qualification of all employed Other females in 1986
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 35.32 29.54 26.56 28.82 32.31 30.78 35.01
(11.38) (9.65) (9.04) (9.37) (10.30) (8.13) (9.21)
Annual income 1986 dollars $10,525 $11,370 $12,451 $11,167 $13,121 $16,842 $18,962
($5,533) ($5,745) ($6,584) ($7,294) ($6,804) ($10,459) ($11,875)
Annual income 1996 dollars $17,291 $18,681 $20,457 $18,347 $21,557 $27,670 $31,154
($9,091) ($9,439) ($10,817) ($11,983) ($11,178) ($17,184) ($19,510)
Hours worked (per week) 37.69 38.16 37.54 36.52 36.66 37.61 37.26
(13.33) (13.20) (12.10) (14.71) (12.73) (13.71) (13.53)
Managerial/administrative occupation 0.53% 0.82% 1.15% 1.50% 1.28% 1.84% 3.14%
Professional 1.43% 2.25% 6.43% 7.75% 28.34% 52.43% 61.18%
Clerical 10.75% 35.63% 55.95% 28.75% 33.49% 27.81% 19.61%
Service 27.83% 18.56% 13.82% 21.50% 14.42% 4.52% 5.49%
Agricultural 4.03% 1.58% 2.40% 2.25% 1.89% 1.17% 0.78%
Product./transport workers 47.00% 30.78% 12.19% 28.25% 14.62% 4.19% 5.49%
Sales 8.42% 10.38% 8.06% 10.00% 5.96% 8.04% 4.31%
Agriculture and fisheries industry 4.16% 2.51% 3.45% 3.00% 2.98% 1.68% 0.78%
Mining 0.08% 0.05% 0.10% 0.00% 0.19% 0.00% 0.39%
Manufacturing 43.56% 31.65% 14.68% 29.25% 16.36% 9.72% 8.63%
Electricity 0.13% 0.31% 0.19% 0.25% 0.12% 0.50% 0.00%
Construction 0.34% 0.61% 1.25% 0.25% 0.89% 0.34% 1.18%
Wholesaling 22.10% 23.98% 23.70% 22.25% 17.67% 16.75% 11.76%
Transportation 4.60% 8.03% 8.54% 8.50% 6.84% 4.02% 2.35%
Finance 2.29% 10.33% 18.23% 10.25% 9.78% 23.79% 9.41%
Social services 22.73% 22.55% 29.85% 26.25% 45.17% 43.22% 65.49%
Married 62.57% 52.56% 39.06% 43.50% 55.03% 57.12% 65.88%
Major urban 90.93% 91.16% 88.20% 93.50% 88.75% 93.47% 91.37%
Semi-urban 5.86% 5.32% 6.81% 3.75% 6.46% 4.69% 4.71%
Rural 3.06% 3.32% 4.70% 2.50% 4.60% 1.84% 3.14%
Sample size 5,236 1,956 1,042 400 2,586 597 255

Standard deviations in parentheses

Table B.9 Income and other employment characteristics by highest educational qualification of all employed Māori males in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 37.00 32.28 30.23 28.35 36.35 35.58 40.77
(11.58) (10.35) (9.62) (10.51) (10.42) (10.41) (10.79)
Annual income 1996 dollars $24,223 $25,625 $27,772 $24,675 $30,802 $38,456 $56,031
($14,561) ($15,192) ($17,658) ($19,930) ($19,088) ($28,317) ($33,492)
Hours worked (per week) 42.46 42.87 43.63 40.30 43.97 44.54 51.40
(14.86) (14.19) (13.94) (16.11) (15.10) (14.83) (15.34)
Managerial/administrative occupation 2.79% 5.43% 8.06% 6.73% 7.20% 11.61% 21.28%
Professional 1.47% 2.59% 3.93% 4.77% 10.55% 48.81% 50.35%
Clerical 3.67% 7.15% 9.35% 8.98% 3.39% 5.54% 4.26%
Service 6.29% 12.70% 18.29% 21.18% 9.07% 6.86% 3.55%
Agricultural 14.03% 11.30% 8.67% 8.70% 7.73% 2.11% 2.84%
Trade orientated 9.65% 11.43% 8.81% 7.43% 26.93% 1.85% 0.71%
Plant and machine operator 35.25% 26.39% 18.50% 14.31% 13.80% 1.06% 2.84%
Elementary/low-skilled 23.63% 17.63% 14.36% 17.53% 10.34% 8.44% 5.67%
Technical 3.22% 5.38% 10.03% 10.38% 10.98% 13.72% 8.51%
Agriculture and fisheries industry 15.10% 12.58% 10.03% 9.68% 9.18% 3.17% 3.55%
Mining 1.00% 0.53% 0.41% 0.14% 0.49% 0.26% 0.00%
Manufacturing 28.49% 25.77% 20.60% 19.50% 19.45% 3.69% 6.38%
Electricity 0.81% 1.03% 0.68% 0.28% 1.38% 0.53% 0.00%
Construction 12.13% 9.70% 8.47% 6.03% 14.72% 0.79% 2.13%
Wholesaling 10.39% 14.51% 16.06% 21.60% 14.08% 7.92% 6.38%
Transportation 10.80% 9.78% 7.11% 4.07% 7.02% 3.43% 1.42%
Finance 3.65% 4.81% 8.06% 9.96% 5.58% 19.00% 9.93%
Social services 17.64% 21.29% 28.59% 28.75% 28.10% 61.21% 70.21%
Married 43.45% 36.00% 30.96% 25.11% 49.56% 50.66% 62.41%
Major urban 59.85% 64.16% 69.11% 72.79% 65.41% 82.32% 78.72%
Semi-urban 22.46% 20.06% 18.29% 15.01% 20.54% 9.50% 11.35%
Rural 17.64% 15.70% 12.40% 12.20% 14.01% 8.18% 9.22%
Sample size 10,309 2,433 1,476 713 2,833 379 141

Standard deviations in parentheses

Appendix B (continued)

Table B.10 Income and other employment characteristics by highest educational qualification of all employed Māori females in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 38.69 34.21 29.78 25.95 36.02 32.56 38.21
(10.69) (10.19) (8.78) (9.74) (11.08) (8.60) (9.80)
Annual income 1996 dollars $16,770 $19,220 $20,898 $15,734 $23,178 $29,994 $42,688
($11,661) ($11,244) ($12,547) ($11,415) ($13,608) ($18,923) ($26,400)
Hours worked (per week) 32.91 34.78 36.07 32.57 36.84 40.68 43.77
(15.71) (15.19) (15.36) (16.26) (16.41) (16.12) (17.93)
Managerial/administrative occupation 4.15% 6.50% 9.60% 4.92% 6.19% 10.20% 15.33%
Professional 3.87% 5.82% 6.42% 7.39% 32.18% 51.56% 51.09%
Clerical 12.53% 27.00% 32.60% 27.65% 16.40% 10.48% 8.03%
Service 23.37% 20.68% 19.20% 28.60% 16.98% 6.80% 3.65%
Agricultural 8.55% 5.59% 4.94% 4.17% 3.80% 0.57% 0.00%
Trade orientated 2.03% 1.68% 1.34% 1.70% 0.97% 0.00% 0.00%
Plant and machine operator 13.21% 7.82% 4.52% 3.79% 2.30% 0.28% 0.00%
Elementary/low-skilled 25.57% 14.55% 9.95% 11.17% 6.23% 8.22% 5.84%
Technical 6.74% 10.36% 11.43% 10.61% 14.94% 11.90% 16.06%
Agriculture and fisheries industry 9.59% 6.09% 6.14% 3.98% 4.16% 1.98% 2.92%
Mining 0.11% 0.09% 0.00% 0.00% 0.00% 0.00% 0.00%
Manufacturing 21.73% 15.32% 8.89% 9.66% 6.06% 1.13% 2.19%
Electricity 0.19% 0.23% 0.49% 0.00% 0.13% 0.28% 0.00%
Construction 1.08% 1.50% 0.78% 0.38% 1.15% 0.28% 1.46%
Wholesaling 20.17% 20.18% 21.95% 27.84% 11.89% 7.08% 4.38%
Transportation 5.32% 5.91% 5.79% 3.41% 3.67% 2.83% 1.46%
Finance 4.45% 9.82% 12.84% 14.58% 7.21% 10.76% 7.30%
Social services 37.37% 40.86% 43.12% 40.15% 65.74% 75.64% 80.29%
Married 47.23% 41.59% 30.84% 15.72% 42.35% 32.01% 42.34%
Major urban 62.92% 65.86% 67.54% 76.33% 66.76% 78.75% 84.67%
Semi-urban 22.07% 18.91% 17.01% 12.88% 17.90% 11.05% 2.92%
Rural 14.99% 15.23% 15.46% 10.80% 15.34% 10.20% 12.41%
Sample size 6,411 2,200 1,417 528 2,262 353 137

Standard deviations in parentheses

Table B.11 –Income and other employment characteristics by highest educational qualification of all employed Part-Māori males in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 35.32 31.69 29.36 26.51 35.39 34.00 38.23
(11.74) (10.67) (9.33) (9.59) (10.54) (9.69) (10.40)
Annual income 1996 dollars $26,044 $28,338 $29,393 $22,945 $34,241 $47,005 $54,415
($16,519) ($19,089) ($20,987) ($20,383) ($20,923) ($33,581) ($34,408)
Hours worked (per week) 44.96 46.12 44.47 39.40 46.60 46.38 47.21
(14.66) (14.19) (14.32) (17.77) (14.45) (15.47) (14.57)
Managerial/administrative occupation 6.09% 10.10% 11.27% 9.84% 11.53% 18.63% 18.05%
Professional 1.41% 2.14% 3.74% 3.59% 9.08% 43.68% 53.58%
Clerical 4.26% 5.78% 9.28% 12.10% 3.28% 6.65% 3.72%
Service 7.27% 13.03% 16.81% 24.59% 10.13% 4.32% 3.72%
Agricultural 14.06% 12.09% 9.28% 8.20% 7.72% 3.55% 2.87%
Trade orientated 14.17% 16.07% 11.81% 5.93% 28.31% 1.55% 1.15%
Plant and machine operator 28.84% 19.49% 13.22% 10.07% 9.57% 1.44% 0.57%
Elementary/low-skilled 19.82% 14.16% 11.52% 13.11% 6.53% 4.21% 4.30%
Technical 4.08% 7.13% 13.07% 12.57% 13.85% 15.96% 12.03%
Agriculture and fisheries industry 14.99% 13.44% 9.52% 8.67% 8.42% 4.77% 3.44%
Mining 0.87% 0.38% 0.29% 0.08% 0.54% 0.33% 0.29%
Manufacturing 26.87% 24.75% 19.63% 12.26% 20.19% 8.76% 5.73%
Electricity 0.68% 0.45% 0.73% 0.47% 1.26% 0.67% 0.86%
Construction 13.01% 11.45% 7.63% 4.84% 14.18% 2.11% 1.72%
Wholesaling 14.58% 18.85% 22.74% 34.35% 17.55% 9.53% 9.17%
Transportation 9.67% 7.25% 6.85% 6.79% 6.36% 3.22% 2.01%
Finance 3.61% 6.31% 11.37% 11.32% 8.19% 32.59% 20.63%
Social services 15.72% 17.12% 21.23% 21.23% 23.30% 38.03% 56.16%
Married 46.27% 38.87% 32.70% 21.39% 52.34% 49.11% 55.01%
Major urban 60.49% 63.91% 71.38% 77.05% 67.95% 81.26% 80.80%
Semi-urban 21.62% 19.19% 15.50% 12.65% 16.26% 8.76% 8.02%
Rural 17.85% 16.90% 13.02% 10.30% 15.70% 9.98% 10.89%
Sample size 5,732 2,663 2,058 1,281 4,274 902 349

Standard deviations in parentheses

Table B.12 Income and other employment characteristics by highest educational qualification of all employed Part-Māori females in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 37.70 33.21 29.50 24.10 34.11 31.99 36.93
(11.08) (10.20) (8.82) (8.06) (10.85) (9.35) (10.50)
Annual income 1996 dollars $17,623 $19,870 $21,164 $15,712 $23,418 $29,503 $38,546
($12,883) ($13,896) ($14,481) ($14,296) ($14,845) ($19,625) ($25,637)
Hours worked (per week) 33.16 34.27 35.18 31.58 36.13 39.99 41.57
(16.44) (15.80) (15.23) (17.40) (15.92) (16.84) (16.25)
Managerial/administrative occupation 7.48% 9.52% 9.68% 6.21% 6.55% 9.78% 11.96%
Professional 2.39% 3.23% 4.60% 3.76% 28.64% 49.14% 59.42%
Clerical 19.61% 31.37% 38.20% 30.23% 19.22% 12.47% 6.88%
Service 25.32% 21.85% 18.75% 34.48% 18.64% 8.07% 3.62%
Agricultural 8.17% 6.32% 5.56% 4.00% 3.95% 2.08% 0.36%
Trade orientated 1.91% 1.55% 1.45% 0.57% 1.16% 0.37% 0.00%
Plant and machine operator 9.77% 5.83% 3.33% 2.94% 1.99% 0.12% 0.00%
Elementary/low-skilled 17.05% 9.38% 5.83% 6.13% 4.34% 3.06% 3.99%
Technical 8.31% 10.96% 12.61% 11.68% 15.52% 14.91% 13.77%
Agriculture and fisheries industry 8.53% 6.88% 6.31% 4.90% 4.53% 2.69% 0.72%
Mining 0.19% 0.14% 0.04% 0.00% 0.06% 0.00% 0.00%
Manufacturing 17.89% 12.79% 10.21% 6.05% 5.47% 4.03% 1.81%
Electricity 0.14% 0.21% 0.39% 0.33% 0.28% 0.37% 0.00%
Construction 1.31% 2.35% 2.32% 0.90% 1.10% 0.49% 0.36%
Wholesaling 27.78% 28.63% 23.70% 39.05% 15.30% 12.35% 6.88%
Transportation 5.13% 5.55% 6.31% 4.66% 3.76% 2.20% 2.54%
Finance 6.93% 14.65% 18.97% 15.11% 10.47% 18.09% 14.86%
Social services 32.10% 28.80% 31.76% 29.00% 59.04% 59.78% 72.83%
Married 52.54% 47.17% 38.06% 16.75% 42.94% 34.35% 44.20%
Major urban 64.41% 65.33% 68.99% 79.17% 68.79% 82.15% 85.51%
Semi-urban 19.82% 19.25% 16.34% 12.25% 16.43% 8.31% 9.78%
Rural 15.76% 15.38% 14.63% 8.58% 14.77% 9.54% 4.71%
Sample size 4,187 2,847 2,283 1,224 3,621 818 276

Standard deviations in parentheses

Appendix B (continued)

Table B.13 – Income and other employment characteristics by highest educational qualification of all employed European males in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad
Age 42.36 36.69 33.50 29.60 39.41 37.84 41.45
(12.27) (11.82) (10.96) (11.86) (11.14) (10.82) (10.17)
Annual income 1996 dollars $31,021 $33,842 $35,988 $28,662 $38,850 $55,051 $63,724
($21,409) ($23,627) ($25,890) ($27,239) ($23,862) ($36,184) ($36,995)
Hours worked (per week) 46.68 47.52 46.21 38.99 46.94 46.31 47.98
(15.17) (14.06) (13.44) (17.91) (13.38) (13.20) (13.60)
Managerial/administrative occupation 11.65% 16.45% 19.61% 15.64% 15.71% 21.17% 18.57%
Professional 1.45% 2.50% 4.63% 5.65% 10.46% 45.53% 56.92%
Clerical 4.47% 6.13% 8.99% 10.43% 2.85% 4.63% 2.71%
Service 6.99% 9.71% 12.15% 20.65% 6.45% 3.22% 1.90%
Agricultural 18.53% 16.58% 12.53% 9.39% 8.48% 4.82% 2.35%
Trade orientated 16.36% 15.67% 10.71% 6.77% 28.94% 1.48% 0.76%
Plant and machine operator 21.61% 14.11% 8.64% 6.13% 6.61% 1.21% 0.62%
Elementary/low-skilled 13.49% 8.96% 7.59% 9.49% 4.45% 2.75% 2.63%
Technical 5.45% 9.89% 15.16% 15.85% 16.05% 15.18% 13.54%
Agriculture and fisheries industry 18.38% 16.73% 12.43% 9.20% 8.73% 5.81% 3.29%
Mining 0.76% 0.44% 0.22% 0.17% 0.45% 0.24% 0.50%
Manufacturing 22.78% 19.26% 15.53% 11.58% 20.08% 9.39% 5.55%
Electricity 0.58% 0.45% 0.44% 0.45% 1.38% 0.93% 0.72%
Construction 11.15% 9.94% 7.12% 5.08% 14.55% 2.04% 1.04%
Wholesaling 18.43% 22.66% 23.64% 32.56% 18.36% 12.12% 7.78%
Transportation 8.96% 7.81% 7.11% 6.26% 6.18% 3.43% 2.16%
Finance 4.84% 8.61% 15.67% 15.61% 10.35% 35.04% 21.32%
Social services 14.12% 14.11% 17.85% 19.10% 19.92% 31.01% 57.64%
Married 62.05% 54.08% 46.70% 29.99% 64.75% 59.43% 67.90%
Major urban 57.53% 62.12% 70.03% 79.11% 69.09% 81.42% 85.25%
Semi-urban 18.77% 16.05% 13.39% 9.42% 15.37% 8.68% 7.36%
Rural 23.66% 21.79% 16.51% 11.39% 15.45% 9.71% 7.06%
Sample size 73,275 34,865 29,154 17,074 87,639 27,077 14,581

Standard deviations in parentheses

Table B.14 –Income and other employment characteristics by highest educational qualification of all employed European females in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad
Age 44.07 38.17 32.61 26.54 38.51 34.48 38.62
(10.63) (10.59) (9.91) (10.79) (11.19) (10.22) (10.21)
Annual income 1996 dollars $18,911 $22,011 $23,047 $15,930 $25,209 $31,368 $38,936
($15,012) ($16,746) ($16,657) ($15,230) ($17,240) ($22,094) ($26,381)
Hours worked (per week) 31.87 33.00 34.10 28.90 34.56 37.82 39.53
(16.19) (15.80) (15.32) (16.89) (16.16) (16.16) (16.33)
Managerial/administrative occupation 10.17% 11.69% 11.27% 7.77% 8.67% 11.61% 11.76%
Professional 1.69% 2.75% 4.68% 4.05% 32.88% 47.23% 56.34%
Clerical 24.73% 36.99% 38.68% 29.17% 17.77% 11.67% 7.25%
Service 25.98% 18.84% 16.37% 33.80% 13.73% 6.33% 3.41%
Agricultural 9.01% 8.50% 7.29% 5.12% 5.87% 3.07% 2.09%
Trade orientated 1.91% 1.35% 1.36% 1.08% 1.03% 0.40% 0.19%
Plant and machine operator 7.36% 3.36% 2.39% 2.30% 1.25% 0.54% 0.29%
Elementary/low-skilled 12.25% 5.80% 4.22% 5.14% 2.46% 2.36% 2.48%
Technical 6.91% 10.72% 13.74% 11.57% 16.34% 16.78% 16.18%
Agriculture and fisheries industry 9.55% 9.32% 8.04% 5.64% 6.34% 3.69% 2.62%
Mining 0.07% 0.09% 0.07% 0.07% 0.04% 0.07% 0.10%
Manufacturing 14.46% 10.75% 9.31% 7.14% 5.27% 4.75% 3.11%
Electricity 0.21% 0.36% 0.41% 0.33% 0.22% 0.32% 0.28%
Construction 2.22% 2.73% 2.34% 1.28% 1.31% 0.70% 0.37%
Wholesaling 30.56% 27.13% 24.43% 38.01% 14.12% 11.80% 7.75%
Transportation 3.82% 4.85% 5.73% 4.54% 3.07% 2.77% 1.55%
Finance 9.71% 18.00% 23.33% 16.80% 12.03% 20.94% 13.73%
Social services 29.39% 26.78% 26.33% 26.18% 57.60% 54.96% 70.48%
Married 68.25% 63.53% 51.04% 24.07% 60.12% 45.87% 52.75%
Major urban 64.96% 66.67% 69.40% 80.05% 70.70% 81.68% 83.96%
Semi-urban 18.48% 15.90% 14.53% 9.44% 13.10% 8.07% 6.85%
Rural 16.56% 17.40% 16.05% 10.48% 16.16% 10.15% 9.08%
Sample size 54,314 40,202 30,461 13,868 67,170 20,750 9,904

Standard deviations in parentheses

Table B.15 – Income and other employment characteristics by highest educational qualification of all employed Other males in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad
Age 37.60 31.91 29.75 25.66 35.96 35.79 39.24
(11.01) (9.71) (8.98) (8.51) (10.05) (8.77) (9.04)
Annual income 1996 dollars $22,859 $26,448 $27,122 $19,916 $31,352 $38,538 $50,531
($14,349) ($18,175) ($19,393) ($20,105) ($20,459) ($29,886) ($36,241)
Hours worked (per week) 42.81 44.07 42.47 34.68 43.38 42.95 44.25
(14.10) (14.00) (14.63) (18.37) (14.19) (15.02) (14.80)
Managerial/administrative occupation 7.97% 9.91% 12.67% 10.21% 13.08% 18.62% 15.99%
Professional 0.95% 1.25% 3.08% 4.36% 11.10% 35.69% 52.26%
Clerical 4.83% 10.54% 13.60% 16.27% 5.86% 7.04% 4.35%
Service 10.81% 12.31% 17.49% 28.24% 10.89% 7.79% 3.01%
Agricultural 5.60% 5.01% 3.89% 3.77% 2.53% 2.06% 1.10%
Trade orientated 15.48% 15.21% 9.88% 5.79% 21.65% 3.02% 1.22%
Plant and machine operator 28.29% 20.74% 13.71% 7.42% 8.81% 4.39% 2.14%
Elementary/low-skilled 23.69% 17.49% 13.54% 12.56% 8.48% 5.45% 4.06%
Technical 2.39% 7.52% 12.14% 11.39% 17.59% 15.95% 15.87%
Agriculture and fisheries industry 5.54% 4.96% 4.07% 4.10% 2.71% 2.37% 1.39%
Mining 0.09% 0.17% 0.06% 0.00% 0.09% 0.16% 0.12%
Manufacturing 40.70% 31.79% 23.18% 13.60% 22.98% 14.11% 7.71%
Electricity 0.22% 0.28% 0.29% 0.20% 0.96% 1.03% 0.81%
Construction 6.09% 7.12% 4.71% 2.86% 8.57% 1.96% 1.56%
Wholesaling 22.68% 25.41% 30.68% 39.30% 25.35% 23.20% 12.80%
Transportation 6.82% 8.38% 8.60% 7.55% 7.28% 5.70% 3.01%
Finance 4.58% 6.78% 10.81% 12.75% 11.55% 25.79% 20.22%
Social services 13.28% 15.10% 17.61% 19.65% 20.51% 25.69% 52.38%
Married 61.38% 49.12% 41.89% 23.49% 61.23% 67.52% 75.43%
Major urban 90.60% 90.03% 91.52% 94.34% 91.61% 94.61% 93.11%
Semi-urban 6.72% 6.04% 5.17% 3.58% 5.56% 3.58% 4.69%
Rural 2.63% 3.93% 3.31% 2.08% 2.71% 1.71% 1.97%
Sample size 6,875 1,755 1,721 1,537 3,325 3,211 1,726

Standard deviations in parentheses

Appendix B (continued)

Table B.16 – Income and other employment characteristics by highest educational qualification of all employed Other females in 1996
  Highest educational qualification
  No qual. S. Cert. UE/SFC Bursary Diploma Bachelor Postgrad.
Age 38.33 32.71 29.36 23.36 34.66 33.67 37.01
(10.39) (9.13) (8.25) (6.73) (10.24) (8.24) (8.92)
Annual Income 1996 sollars $17,287 $21,489 $22,240 $13,779 $22,992 $26,858 $34,278
($11,910) ($12,140) ($13,816) ($12,934) ($14,796) ($20,369) ($26,213)
Hours worked (per week) 37.29 36.37 36.85 28.44 35.77 36.20 37.35
(15.64) (14.08) (15.56) (17.39) (14.89) (15.10) (15.61)
Managerial/administrative occupation 7.65% 9.08% 9.47% 4.82% 6.86% 10.83% 9.94%
Professional 1.12% 2.80% 3.43% 3.28% 25.86% 34.86% 52.93%
Clerical 10.61% 36.55% 40.12% 34.67% 26.65% 18.76% 9.94%
Service 23.41% 19.25% 21.18% 34.96% 16.87% 10.87% 6.52%
Agricultural 4.51% 1.31% 1.56% 1.46% 1.43% 0.93% 0.55%
Trade orientated 3.22% 2.23% 1.25% 1.09% 0.86% 0.56% 0.00%
Plant and machine operator 20.90% 7.54% 3.93% 2.85% 3.02% 3.22% 0.44%
Elementary/low-skilled 25.59% 12.96% 7.66% 7.15% 5.11% 4.59% 3.20%
Technical 3.00% 8.28% 11.40% 9.71% 13.34% 15.38% 16.46%
 Agriculture and fisheries industry 4.73% 1.60% 1.93% 1.24% 1.78% 1.37% 0.55%
Mining 0.00% 0.00% 0.00% 0.00% 0.06% 0.00% 0.00%
Manufacturing 33.39% 18.22% 11.03% 7.81% 6.99% 9.02% 3.76%
Electricity 0.12% 0.51% 0.44% 0.29% 0.38% 0.44% 0.33%
Construction 0.55% 1.09% 1.00% 0.51% 0.41% 0.56% 0.11%
Wholesaling 27.71% 26.44% 27.73% 40.22% 20.33% 19.36% 15.03%
Transportation 3.08% 6.11% 6.17% 5.40% 5.11% 4.95% 3.20%
Finance 5.31% 17.53% 21.74% 17.15% 15.95% 25.48% 16.46%
Social services 25.12% 28.50% 29.97% 27.37% 48.98% 38.81% 60.55%
Married 66.67% 52.88% 43.99% 16.72% 56.39% 59.82% 65.64%
Major urban 91.33% 92.06% 92.96% 96.42% 91.20% 93.12% 94.14%
Semi-urban 6.14% 5.94% 5.11% 2.26% 5.27% 4.15% 3.31%
Rural 2.51% 2.00% 1.93% 1.24% 3.53% 2.58% 2.54%
Sample size 5,100 1,751 1,605 1,370 3,148 2,484 905

Standard deviations in parentheses

Appendix C  Additional Regression Results

Table C.1 – Income effects of secondary and tertiary education of males: 1986 and 1996
  Māori males Part-Māori males European males Other males
  1986 1996 1986 1996 1986 1996 1986 1996
  1986 1996 1986 1996 1986 1996 1986 1996
  Māori males Part-Māori males European males Other males
Intercept 8.9329 8.7321 8.8399 8.6738 8.9331 8.8250 8.8431 8.6581
(326.95) (225.61) (168.05) (215.38) (1033.34) (836.09) (188.35) (162.12)
School Certificate 0.0822 0.1296 0.0690 0.1202 0.0473 0.0704 0.0495 0.1820
(8.63) (9.31) (4.09) (8.45) (15.59) (18.03) (3.52) (9.85)
U.E./Sixth Form Cert. 0.1541 0.2497 0.1258 0.1991 0.0840 0.1354 0.1334 0.2640
(9.32) (13.77) (5.33) (11.88) (22.35) (30.97) (7.17) (13.73)
Bursary 0.1839 0.1936 0.0731 0.0501 0.0249 -0.0434 -0.0247 0.0545
(5.05) (6.70) (1.69) (2.14) (3.63) (7.00) (0.84) (2.29)
Diploma 0.2267 0.2364 0.2422 0.2504 0.1784 0.1561 0.1937 0.2508
(29.60) (16.69) (18.02) (18.66) (83.42) (48.19) (17.21) (16.08)
Bachelor's degree 0.3864 0.3482 0.3926 0.4424 0.3303 0.3440 0.3235 0.2136
(8.21) (8.15) (10.31) (16.70) (67.55) (64.49) (13.38) (10.38)
Postgraduate qual. 0.4778 0.6497 0.4444 0.5954 0.4077 0.4691 0.4154 0.3790
(10.62) (11.97) (7.04) (17.14) (74.45) (73.49) (13.96) (14.69)
Hours worked (per week) 0.0056 0.0105 0.0049 0.0111 0.0050 0.0107 0.0091 0.0127
(19.17) (25.94) (8.49) (26.75) (48.36) (97.38) (19.18) (29.20)
Managerial/administrative occupation 0.1487 0.1209 0.1840 0.2336 0.2470 0.2948 0.3610 0.1357
(5.57) (4.38) (5.16) (9.80) (63.89) (53.83) (11.80) (5.57)
Professional 0.0364 0.0814 0.1246 0.1419 0.1119 0.2285 0.2056 0.3972
(1.74) (2.82) (4.14) (5.37) (30.11) (38.33) (9.02) (16.06)
Service 0.0413 -0.0292 0.1029 0.0025 -0.0472 -0.0051 -0.1620 -0.2295
(2.24) (1.20) (3.24) (0.10) (9.78) (0.82) (6.60) (9.94)
Agricultural -0.2421 -0.2577 -0.1894 -0.1417 -0.2794 -0.1352 -0.3434 -0.2367
(10.95) (8.49) (4.26) (4.49) (42.63) (16.55) (8.45) (5.37)
Product./transport workers -0.1518 -0.1031 -0.1212 -0.1413
(11.56)   (4.22)   (39.63)   (8.11)  
Sales -0.0674 0.0056 0.0157 -0.0430
(2.80)   (0.17)   (3.88)   (1.68)  
Trade orientated -0.0754 -0.0307 -0.0300 -0.0774
  (3.36)   (1.38)   (5.49)   (3.47)
Plant and machine operator -0.0825 -0.0370 -0.0616 -0.1312
  (4.07)   (1.70)   (10.86)   (6.22)
Elementary/low-skilled -0.2231 -0.1755 -0.1759 -0.2599
  (10.41)   (7.59)   (27.78)   (11.54)
Technical 0.0580 0.1154 0.1730 0.1580
  (2.20)   (4.86)   (31.11)   (6.76)
Mining industry 0.1882 0.2589 0.3367 0.3324 0.1804 0.2581 0.1463 0.5571
(6.56) (4.63) (5.35) (5.79) (16.35) (17.03) (1.01) (6.24)
Manufacturing 0.1189 0.1955 0.1726 0.1764 0.0949 0.1332 0.0481 0.2142
(6.48) (7.98) (4.54) (6.81) (15.22) (19.22) (1.35) (5.21)
Electricity 0.1219 0.2830 0.1764 0.2282 0.1420 0.2781 0.1021 0.5225
(5.52) (7.53) (3.71) (5.27) (18.35) (27.06) (2.17) (9.49)
Construction 0.0064 0.0631 0.0715 0.0562 0.0564 0.0460 0.0808 0.1396
(0.33) (2.35) (1.77) (1.99) (8.62) (6.16) (2.12) (3.07)
Wholesaling -0.0920 -0.0505 -0.0328 -0.0543 -0.0525 -0.0609 -0.0610 -0.0541
(4.45) (1.91) (0.81) (1.99) (8.16) (8.51) (1.64) (1.29)
Transportation 0.1204 0.1319 0.1889 0.1905 0.1551 0.1633 0.1221 0.2313
(6.21) (4.84) (4.74) (6.50) (23.97) (21.29) (3.27) (5.26)
Finance 0.0413 0.1088 0.0941 0.1907 0.1642 0.2154 0.0575 0.2712
(1.41) (3.53) (1.92) (6.35) (24.10) (29.02) (1.41) (6.29)
Social services -0.1096 -0.0256 -0.0264 -0.0101 0.0245 -0.0098 -0.0045 0.0811
(5.60) (1.02) (0.65) (0.38) (3.93) (1.40) (0.12) (1.95)
Married 0.1478 0.1740 0.1772 0.1502 0.1479 0.1443 0.0815 0.0511
(23.09) (17.29) (13.27) (14.17) (69.64) (55.07) (8.08) (4.30)
Major urban -0.0321 0.0004 -0.0070 0.0580 0.0506 0.0847 -0.1050 -0.0702
(4.38) (0.04) (0.52) (4.67) (22.04) (27.55) (6.51) (3.26)
Rural -0.0933 -0.0945 -0.1028 -0.0726 -0.0763 -0.0356 -0.0937 -0.0002
(9.44) (9.62) (5.11) (4.19) (21.76) (8.08) (2.94) (0.00)
Experience 0.04275 0.06325 0.04762 0.06839 0.04306 0.06117 0.03599 0.05819
(39.03) (36.34) (23.58) (39.24) (139.92) (149.89) (22.66) (31.01)
Experience2 -0.00079 -0.00111 -0.00089 -0.00128 -0.00077 -0.00111 -0.00061 -0.00101
(32.70) (30.88) (18.98) (32.74) (120.46) (131.88) (17.42) (24.59)
F 358.07 275.86 157.05 370.19 6574.08 6229.73 221.60 350.78
Sample size 28,659 18,219 8,173 17,128 335,632 281,246 17,273 19,930

Dependent variable: The natural logarithm of annual income (t-ratios in parentheses)

Dependent variable: The natural logarithm of annual income

Least squares regression (t-ratios based on White consistent standard errors, in parentheses)

Appendix C  Additional Regression Results (continued)

Table C.2 – Income effects of secondary and tertiary education of females: 1986 and 1996
  Māori females Part-Māori females European females Other females
  1986 1996 1986 1996 1986 1996 1986 1996
Intercept 8.1840 8.4494 7.9040 8.3508 8.1418 8.5343 8.2951 8.3268
(175.73) (168.81) (86.28) (152.28) (545.03) (592.14) (106.71) (115.63)
School Certificate 0.0718 0.1266 0.0659 0.0959 0.0620 0.0826 0.0547 0.1973
(4.88) (7.59) (2.64) (5.59) (14.27) (17.39) (3.14) (10.16)
U.E./Sixth Form Cert. 0.1626 0.2644 0.1585 0.1825 0.0925 0.1475 0.1292 0.2407
(7.75) (12.90) (4.98) (9.23) (16.88) (27.00) (5.66) (10.96)
Bursary 0.0656 0.0885 0.1521 0.0458 0.0366 -0.0706 -0.0320 -0.0210
(1.28) (2.52) (2.63) (1.74) (3.53) (9.31) (0.78) (0.79)
Diploma 0.2524 0.2703 0.2308 0.2528 0.1614 0.1788 0.1308 0.1696
(16.15) (14.42) (8.26) (14.01) (36.55) (37.26) (6.95) (8.73)
Bachelor's degree 0.4269 0.4903 0.4490 0.3553 0.2832 0.2853 0.2175 0.2103
(7.48) (12.46) (7.24) (11.87) (32.81) (42.13) (5.70) (9.03)
Postgraduate qual. 0.3189 0.7290 0.4159 0.5229 0.3688 0.4371 0.3163 0.3488
(2.85) (13.54) (5.81) (12.12) (35.34) (52.18) (5.71) (10.57)
Hours worked (per week) 0.0189 0.0141 0.0208 0.0170 0.0224 0.0206 0.0198 0.0179
(31.74) (31.54) (21.29) (36.93) (136.66) (165.75) (30.02) (37.67)
Managerial/administrative occupation 0.0366 0.0805 0.2223 0.1074 0.2748 0.1692 0.1672 0.0976
(0.60) (2.84) (3.64) (4.62) (28.64) (31.30) (2.41) (3.84)
Professional -0.0158 0.0525 0.0917 0.0852 0.1108 0.1708 0.1102 0.2541
(0.70) (2.29) (2.68) (4.11) (22.85) (34.50) (4.43) (12.09)
Service -0.3270 -0.2607 -0.2102 -0.2886 -0.3373 -0.2879 -0.3209 -0.2820
(19.08) (13.28) (7.29) (16.04) (69.34) (61.39) (15.86) (14.68)
Agricultural -0.4316 -0.3653 -0.3126 -0.2013 -0.4204 -0.2218 -0.4278 -0.2286
(10.73) (8.49) (3.76) (4.13) (30.68) (17.45) (5.62) (3.40)
Product./transport workers -0.2971 -0.2047 -0.2682 -0.2537
(18.25)   (6.28)   (49.30)   (10.71)  
Sales -0.3018 -0.2228 -0.2038 -0.2999
(10.63)   (6.04)   (37.50)   (9.57)  
Trade orientated -0.2276 -0.2415 -0.2021 -0.1635
  (4.82)   (4.95)   (15.47)   (3.89)
Plant and machine operator -0.2162 -0.2875 -0.3223 -0.2630
  (8.55)   (10.18)   (37.89)   (10.39)
Elementary/low-skilled -0.3577 -0.3285 -0.3666 -0.3226
  (17.02)   (14.13)   (50.08)   (14.17)
Technical 0.0218 0.0280 0.0505 0.1296
  (1.02)   (1.51)   (15.91)   (6.09)
Mining industry 0.2301 0.6455 0.3797 0.7021 0.1150 0.1007 0.1116 0.4539
(2.74) (4.10) (3.15) (3.19) (3.49) (1.79) (0.56) (2.83)
Manufacturing 0.2211 0.2998 0.3100 0.2578 0.1508 0.0965 0.0044 0.2311
(6.00) (7.32) (3.76) (5.34) (11.84) (7.68) (0.06) (3.71)
Electricity 0.2577 0.4976 0.3837 0.3934 0.2470 0.2017 0.0309 0.4829
(3.01) (6.91) (3.23) (3.63) (11.42) (8.21) (0.21) (5.07)
Construction 0.0141 0.1370 0.0917 0.1166 0.0198 0.0438 -0.1552 0.1986
(0.22) (1.97) (0.82) (1.76) (1.17) (2.63) (1.63) (2.10)
Wholesaling 0.0266 0.0259 0.1796 0.0370 0.0275 -0.0784 -0.0866 0.0010
(0.70) (0.63) (2.23) (0.78) (2.19) (6.44) (1.26) (0.02)
Transportation 0.1572 0.2836 0.3491 0.3239 0.1917 0.1613 0.0323 0.3238
(3.95) (6.18) (4.23) (6.26) (14.60) (11.89) (0.46) (4.90)
Finance 0.1084 0.2899 0.3273 0.3098 0.1746 0.1663 0.1074 0.3357
(2.59) (6.64) (4.06) (6.41) (13.82) (13.53) (1.55) (5.32)
Social services 0.0739 0.0889 0.1917 0.1079 0.0715 -0.0658 -0.0479 0.1388
(2.03) (2.23) (2.47) (2.30) (5.79) (5.50) (0.71) (2.23)
Married -0.1436 -0.1069 -0.2083 -0.1480 -0.1983 -0.1677 -0.1047 -0.1212
(12.25) (8.45) (9.88) (11.90) (63.36) (55.60) (7.83) (9.72)
Major urban 0.0675 0.1043 0.0497 0.1488 0.0788 0.1208 0.0642 0.0832
(5.21) (6.99) (2.08) (9.67) (19.16) (29.61) (2.38) (3.34)
Rural -0.0545 -0.0495 -0.0476 0.0383 -0.0423 0.0378 -0.0615 0.0309
(2.83) (2.37) (1.33) (1.73) (6.78) (6.43) (1.27) (0.63)
Experience 0.03044 0.05259 0.04108 0.05432 0.02375 0.04219 0.02733 0.04930
(124.84) (25.79) (15.24) (28.94) (55.83) (89.15) (13.29) (24.39)
Experience2 -0.00047 -0.00089 -0.00073 -0.00095 -0.00032 -0.00068 -0.00041 -0.00081
(12.30) (20.47) (11.04) (21.95) (33.42) (65.94) (8.47) (17.82)
F 213.81 203.44 86.98 276.80 3687.49 4764.87 126.78 274.08
Sample size 16,898 13,241 6,007 15,136 228,523 234,079 12,031 16,156

Dependent variable: The natural logarithm of annual income

Least squares regression (t-ratios based on White consistent standard errors, in parentheses)

Appendix D  Alternative Decompositions

 

Table D.1 – Decompositions of income differentials based on alternative specifications: all employed males and females
Income differential explained by: 1986 1996
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Males            
Overall difference 0.25064 0.16121 0.22514 0.35388 0.23317 0.31301
Alternative  weights:             
Effect of characteristics 0.16509 0.13680 0.09981 0.22114 0.17654 0.13153
 

 

(65.9%) (84.9%) (44.3%) (62.5%) (75.7%) (42.0%)
Effect of coefficients 0.08542 0.0244 0.12533 013274 0.05663 0.18148
( b0E – b0M/P/O )+

 

(34.1%) (15.1%) (515.7%) (37.5%) 24.3%) (58.0%)
 Equation 2 weights, reported in Table 3:             
Effect of characteristics 0.18199 0.14181 0.04331 0.23184 0.18064 0.04812
 

 

 9
(72.6%) (88.0%) (19.2%) (65.5%) (77.5%) (15.4%)
Effect of coefficients 0.06865 0.0194 0.18183 0.12204 0.05253 0.26489
 ( b0E – b0M/P/O )+

 

(27.4%) (12.0%) (80.8%) (34.5%) (22.5%) (84.6%)
Females            
Overall difference 0.09795 0.04198 0.00642 0.15531 0.10585 0.08972
Alternative weights:             
Effect of characteristics 0.08997 0.03045 0.00590 0.13769 0.08893 0.04932
 

 

(91.9%) (72.5%) (91.9%) (88.7%) (84.0%) (55.0%)
Effect of coefficients 0.00798 0.01153 0.00052 0.01762 0.01692 0.0404
  ( b0E – b0M/P/O )+

 

(8.1%) (27.5%) (8.1%) (11.3%) (16.0%) (45.0%)
Equation 2 weights, reported in Table 3:             
Effect of characteristics 0.09007 0.02963 -0.00510 0.11610 0.08367 0.02149
 

 

(92.0%) (70.6%) (-79.5%) (74.7%) (79.0%) (24.0%)
Effect of coefficients 0.00788 0.01235 0.01152 0.03921 0.02218 0.06823

  ( b0E – b0M/P/O )+

 

 

 

(8.0%) (29.4%) (179.5%) (25.3%) (21.0%) (76.0%)